Files
pytorch/caffe2/python/net_builder.py
Orion Reblitz-Richardson 6223bfdb1d Update from Facebook (#6692)
* [GanH][Easy]: Add assertion to adaptive weighting layer

0 weight causes numeric instability and exploding ne

* [Easy] Add cast op before computing norm in diagnose options

As LpNorm only takes floats we add a manual casting here.

* Introduce a new caching device allocator

`cudaMalloc` and `cudaFree` calls are slow, and become slower the
more GPUs there are. Essentially, they grab a host-wide (not device-wide) lock
because GPU memory is transparently shared across all GPUs. Normally, this
isn't much of a concern since workloads allocate memory upfront, and reuse it
during later computation.

However, under some computation models (specifically, memory conserving
approaches like checkpoint-and-recompute, see
https://medium.com/@yaroslavvb/fitting-larger-networks-into-memory-583e3c758ff9)
this assumption is no longer true. In these situations, `cudaMalloc` and
`cudaFree` are common and frequent. Furthermore, in data parallel contexts,
these calls happen at nearly the same time from all GPUs worsening lock
contention.

A common solution to this problem is to add a custom allocator. In fact,
nVIDIA provides one out of the box: CUB, which Caffe2 already supports.
Unfortunately, the CUB allocator suffers from very high fragmentation. This is
primarily because it is a "buddy" allocator which neither splits nor merges
free cached blocks. Study
https://github.com/NVlabs/cub/blob/1.8.0/cub/util_allocator.cuh#L357 if you
want to convince yourself.

This diff adapts a caching allocator from the Torch codebase
https://github.com/torch/cutorch/blob/master/lib/THC/THCCachingAllocator.cpp
which does splitting and merging and ends up working really well, at least for
workloads like the checkpoint-and-recompute computation models noted above.

I simplified the implementation a little bit, made it a bit more C++-like. I
also removed a bunch of stream synchronization primitives for this diff. I
plan to add them back in subsequent diffs.

* Report reader progress in fblearner workflows

Integrate with fblearner progress reporting API and add support to report training progress from reader nodes.
If reader is constructed with batch limits, report based on finished batch vs total batch. The finished batch may be more than total batch because we evaludate if we should stop processing everytime we dequeue a split.
If no limit for the reader, report based on finished splits (Hive files) vs total splits. This is fairly accurate.

* [GanH][Diagnose]: fix plotting

1. ganh diagnose needs to set plot options
2. modifier's blob name is used for metric field can need to be fixed before
generating net

* Automatic update of fbcode/onnx to 985af3f5a0f7e7d29bc0ee6b13047e7ead9c90c8

* Make CompositeReader stops as soon as one reader finishes

Previously, CompositeReader calls all readers before stopping. It results in flaky test since the last batch may be read by different threads; resulting in dropped data.

* [dper] make sure loss is not nan

as desc.

* [rosetta2] [mobile-vision] Option to export NHWC order for RoIWarp/RoIAlign

Thanks for finding this @stzpz and @wangyanghan. Looks like NHWC is more
optimized. For OCR though it doesn't yet help since NHWC uses more mem b/w but
will soon become important.

* Intra-op parallel FC operator

Intra-op parallel FC operator

* [C2 Proto] extra info in device option

passing extra information in device option

design doc: https://fb.quip.com/yAiuAXkRXZGx

* Unregister MKL fallbacks for NCHW conversions

* Tracing for more executors

Modified Tracer to work with other executors and add more tracing

* Remove ShiftActivationDevices()

* Check for blob entry iff it is present

When processing the placeholders ops, ignore if the blob is not present in the blob_to_device.

* Internalize use of eigen tensor

Move use of eigen tensor out of the header file so we don't get template partial specialization errors when building other libraries.

* feature importance for transformed features.

* - Fix unused parameter warnings

The changes in this diff comments out unused parameters.
This will allow us to enable -Wunused-parameter as error.

#accept2ship

* add opencv dependencies to caffe2

The video input op requires additional opencv packages. This is to add them to
cmake so that it can build

* Add clip_by_value option in gradient clipping

Add clip_by_value option in gradient clipping

when the value is bigger than max or smaller than min, do the clip

* std::round compat
2018-04-17 23:36:40 -07:00

743 lines
27 KiB
Python

## @package net_builder
# Module caffe2.python.net_builder
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, context
from caffe2.python.task import Task, TaskGroup
from caffe2.python.control_ops_util import add_if_op, add_while_op
@context.define_context()
class NetBuilder(object):
"""
Scope-driven mechanism for building nets, loops and conditional blocks.
Arguments:
name: NetBuilder's name
initial_scope: list of blobs that are available for reading/writing
Example:
from caffe2.python.net_builder import NetBuilder, ops
with NetBuilder() as nb:
c = ops.Const(5)
d = ops.Const(0)
with ops.loop():
ops.stop_if(ops.LE([c, ops.Const(0)]))
ops.Add([c, ops.Const(-1)], [c])
with ops.If(ops.GE([c, ops.Const(3)])):
ops.Add([d, ops.Const(10)], [d])
ops.Print(c, [])
ops.Print(d, [])
step = core.to_execution_step(nb)
"""
def __init__(self, name=None, initial_scope=None, _stop_blob_required=False,
_stop_blob=None, _fullname=None, _use_control_ops=False):
parent = NetBuilder.current(required=False)
assert not _fullname or not name, 'Cannot set both _fullname and name'
assert not _use_control_ops or \
(not _stop_blob_required and not _stop_blob), \
'Stop blobs are not used with control operators'
self.name = _fullname or '/'.join(
n for n in (parent.name if parent else None, name) if n
)
self._frozen = False
self._current_net = None
self._children = []
if parent:
# make sure parent has an up to date lexical scope computed
parent._update_lexical_scope()
self._init_lexical_scope = set(parent._lexical_scope) if parent else set()
if initial_scope:
self._init_lexical_scope |= set([str(b) for b in initial_scope])
self._lexical_scope = set(self._init_lexical_scope)
self._stop_blob = _stop_blob
self._stop_blob_required = _stop_blob_required
self._use_control_ops = _use_control_ops
def stop_blob(self):
"""
Returns the BlobReference to the stop_blob of this NetBuilder.
If one is not yet available, creates one.
This function assumes that the stop_blob() will be used immediatelly
in the current net, so it doesn't initialize it if the current net is
the first of the builder.
"""
assert not self._use_control_ops, \
'Stop blobs are not used with control operators'
if self._stop_blob is None:
net = self.current_net()
self._stop_blob = core.BlobReference(
net.NextName('stop_blob'), net=net)
net.Const(False, blob_out=self._stop_blob)
if self._current_net != self._children[0]:
self._children.insert(0, core.Net('stop_blob_init'))
self._children[0].Const(False, blob_out=self._stop_blob)
return self._stop_blob
def stop_if(self, blob):
assert not self._use_control_ops, \
'Stop blobs are not used with control operators'
stop_blob = self.stop_blob()
ops.Or([stop_blob, blob], [stop_blob])
self._current_net = None
def _assert_mutable(self):
assert not self._frozen, (
'This NetBuilder (%s) has been built already.' % self.name)
def _update_lexical_scope(self):
"""
Updates lexical scope based on the current list of children.
Lexical scope contains names of blobs that are currently available
and were introduced in the net builder
"""
self._lexical_scope = set(self._init_lexical_scope)
for child in self._children:
if isinstance(child, core.Net):
self._lexical_scope |= child.UsedBlobNames()
elif isinstance(child, NetBuilder) and child._use_control_ops:
self._lexical_scope |= child._lexical_scope
def _reset_children(self):
self._current_net = None
self._children = []
self._lexical_scope = set(self._init_lexical_scope)
def add(self, child):
self._assert_mutable()
if self._use_control_ops:
assert isinstance(child, core.Net) or (
isinstance(child, NetBuilder) and child._use_control_ops), \
"Expected Net or NetBuilder with control ops"
self._current_net = None
self._children.append(child)
# to-do : check it's not a dag net
if isinstance(child, core.Net):
self._current_net = child
self._update_lexical_scope()
return child
def current_net(self, name=None):
self._assert_mutable()
if self._current_net is None or name is not None:
self.add(core.Net(name))
return self._current_net
def freeze(self):
for child in self._children:
if hasattr(child, 'freeze'):
child.freeze()
self._current_net = None
self._frozen = True
def get(self):
self.freeze()
return self._children
def __exit__(self, etype, *args):
if self._use_control_ops and len(self._children) > 0:
_children = self._children
self._reset_children()
merged_net = NetBuilder.merge_nets(
_children, self._lexical_scope)
assert merged_net, "Expected a non-empty merge of children"
self._children = [merged_net]
self.freeze()
if etype is not None:
return
assert (not self._stop_blob_required) or self._stop_blob is not None, (
'This NetBuilder (%s) requires a stop condition ' % self.name +
'to be set with `stop` or `stop_if`')
@staticmethod
def merge_nets(nets_or_builders, outer_blob_names):
# Only nets or builders with control ops are allowed.
# Need to pay attention to external outputs, e.g.
# ...
# IfNet1 (cond_blob):
# (Net1)
# X = 1
# IfNet2 (...):
# X = X + 1
# ...
# In this example there're two children in then branch of IfNet1:
# a subnet Net1 that creates blob X and sets its value to one, and
# a net builder IfNet2 that (conditionally) increments X.
# From IfNet2's point of view X is an external input
# and output blob, it will be put into IfNet2 net's external_output.
# At the same time, from the point of view of IfNet1 X is purely local.
# Net.AppendNet just merges external outputs of the networks, so
# without checking this the result of Net1.AppendNet(IfNet2's net)
# would have blob X in external_output
net = None
for n in nets_or_builders:
cur = None
if isinstance(n, NetBuilder):
assert n._use_control_ops, \
"Merging of NetBuilder supported only for control ops"
nets = n.get()
assert len(nets) == 1 and isinstance(nets[0], core.Net), \
"Invalid control op net builder"
cur = nets[0]
else:
assert isinstance(n, core.Net)
cur = n
if net:
net.AppendNet(cur)
else:
net = cur
if net:
# correct external output
external_outputs = [o for o in net.Proto().external_output
if o in outer_blob_names]
net.Proto().external_output[:] = external_outputs
return net
def __str__(self):
return self.name or 'Un-named NetBuilder'
class Operations(object):
"""
Operations to be used in the context of a NetBuilder.
"""
def net(self, net=None, name=None):
"""
Retrieves the current net, or add a new net to the builder.
Args:
net: If provided, add the given net to the active builder.
Else, returns the current Net or creates a new one as needed.
name: if provided, creates a new Net with given name and makes
it the new current net of the active builder. Cannot
be provided if net is provided.
"""
assert name is None or net is None, (
'Cannot provide both `net` and `name`.')
if net is not None:
NetBuilder.current().add(net)
return net
return NetBuilder.current().current_net(name=name)
def __getattr__(self, op_type):
"""
Adds an operator call to the currently active Net.
"""
if op_type.startswith('__'):
raise AttributeError()
# We want hasattr to work properly even if no context is active.
if NetBuilder.current(required=False) is None:
raise AttributeError('No active NetBuilder.')
return getattr(self.net(), op_type)
def task_group(self):
"""
Creates a local task group which will execute as the next step of
the current NetBuilder.
"""
from caffe2.python import task
group = NetBuilder.current()
with task.Cluster():
with task.Node('local'):
tg = task.TaskGroup()
group.add(tg)
return tg
def stop(self):
"""
Stop execution of the current execution step.
Example:
ops.Print(a, 0)
ops.stop()
ops.Print(b, 0)
In the example, 'b' will never be printed.
"""
return self.stop_if(ops.Const(True))
def stop_if(self, blob):
"""
Stop execution of the current execution step if the
condition `blob` is met.
Example:
ops.Print(a, 0)
ops.stop_if(ops.LE([x, ops.Const(0)]))
ops.Print(b, 0)
In the example, 'b' will only be printed if the value of scalar
tensor 'x' is greater than 0.
"""
return NetBuilder.current().stop_if(blob)
def loop(self, iters=None, name=None):
"""
Creates a NetBuilder that will execute in a loop as the next step of
the current NetBuilder. If `iters` is provided, the loop will execute
for `iters` iterations and then stop. `iters` can be a constant or a
BlobReference. If `iters` is not provided, the loop will execute
until `ops.stop` or `ops.stop_if` is called.
Examples:
a = ops.Const(5)
with ops.loop():
ops.stop_if(ops.LE([a, ops.Const(0)]))
ops.Print(a, 0)
ops.Add([a, ops.Const(-1)], [a])
Above, 'a' will be printed 5 times, with values 5 to 1.
with ops.loop(10) as loop:
ops.LogInfo(loop.iter())
This will print the numbers from 0 to 9.
x = ops.Add([ops.Const(10), ops.Const(10)])
with ops.loop(x) as loop:
ops.LogInfo(loop.iter())
This will print the numbers from 0 to 19.
"""
return NetBuilder.current().add(_Loop(iters, name=name))
def stop_guard(self, has_stopped_blob=None, name=None):
"""
Creates a NetBuilder that will execute once as the next step of the
current NetBuilder. After execution, a bool tensor will indicate
whether the inner execution was halted with `stop` or `stop_if`.
Example:
a = ops.Const(True)
with ops.stop_guard() as sg1:
ops.stop_if(a)
ops.Print(ops.Const('did not stop'))
b = ops.Const(False)
with ops.stop_guard() as sg2:
ops.stop_if(b)
ops.Print(ops.Const('did not stop'))
ops.Print(sg1.has_stopped(), [])
ops.Print(sg2.has_stopped(), [])
In the example, 'did not stop' will be printed once,
followed by True and False.
"""
return NetBuilder.current().add(
_StopGuard(has_stopped_blob=has_stopped_blob, name=name))
def If(self, cond, name=None):
"""
Creates a NetBuilder that will execute once as the next step of the
current NetBuilder if the blob `cond` is True.
Example:
with ops.If(ops.Const(True)):
ops.Print(ops.Const('Will print'))
with ops.If(ops.Const(False)):
ops.Print(ops.Const('Wont print'))
The example will print 'Will print' once.
"""
return NetBuilder.current().add(_RunIf(cond, name=name))
def IfNet(self, cond, name=None):
"""
Same as If, but uses 'If' operator instead of execution step logic
"""
return NetBuilder.current().add(_RunIfNet(cond, name=name))
def Else(self, name=None):
"""
Else branch of IfNet, has to be specified immediately after IfNet.
Example:
with ops.IfNet(ops.LT([x, y])):
...
with ops.Else():
...
"""
return _RunElseNet(name=name)
def WhileNet(self, name=None):
"""
NetBuilder for 'While' control operator
"""
return NetBuilder.current().add(_RunWhileNet(name=name))
def Condition(self, name=None):
"""
Loop's condition, executed within WhileNet context
"""
assert isinstance(NetBuilder.current(), _RunWhileNet), \
"Use of Condition outside of WhileNet"
return _RunWhileCondition(name=name)
def task_init(self):
"""
Defines operations that will be executed once at task startup.
Useful when implementing processors, that don't have access to the Task
top-level structure.
This setup will be run only once, even if multiple instances of the task
will run in parallel. For instance-local initialization, use
`task_instance_init` instead.
Example:
def my_processor(rec):
with ops.task_init():
one = ops.Const(1)
two = ops.Const(1)
return Tuple(
ops.Add(rec[0](), zero), ops.Add(rec[1](), two))
"""
setup = _SetupBuilder(_SetupBuilder.INIT)
self.net().add_attribute(Task.TASK_SETUP, setup)
return setup
def task_exit(self):
"""
Define operations to be executed once at task shutdown.
Useful when implementing processors, that don't have access to the Task
top-level structure.
This shutdown will be run only once, after all concurrent instances of
the task have already finished. For instance-local shutdown,
use `task_instance_exit` instead.
Example:
def read_queue(queue):
with ops.task_exit():
queue.close(ops.net())
return queue.read(ops.net())
"""
setup = _SetupBuilder(_SetupBuilder.EXIT)
self.net().add_attribute(Task.TASK_SETUP, setup)
return setup
def task_instance_init(self):
"""
Defines operations that will be executed once at startup of each
instance of a task. This can be seen as "thread_local" initialization.
It is guaranteed to run only after all `task_init` logic finishes.
This setup will be run concurrently for each instance of a task.
For global task initialization, use `task_init` instead.
"""
setup = _SetupBuilder(_SetupBuilder.INIT)
self.net().add_attribute(Task.TASK_INSTANCE_SETUP, setup)
return setup
def task_instance_exit(self):
"""
Defines operations that will be executed once at shutdown of each
instance of a task. This can be seen as "thread_local" finalization.
This shutdown will be run concurrently for each instance of a task.
For global task shutdown, use `task_exit` instead.
"""
setup = _SetupBuilder(_SetupBuilder.EXIT)
self.net().add_attribute(Task.TASK_INSTANCE_SETUP, setup)
return setup
def local_init(self):
"""
Similar to `task_init`, but executes at TaskGroup's startup instead,
before any task of the group starts executing. This will run only
once on each node, before initialization of any task, so it can be
used e.g. to initialize blobs shared across tasks.
"""
setup = _SetupBuilder(_SetupBuilder.INIT)
self.net().add_attribute(TaskGroup.LOCAL_SETUP, setup)
return setup
def local_exit(self, name=None):
"""
Similar to `task_exit`, but executes at TaskGroup's exit instead,
after all tasks of the group finished execution.
This will run only once on each node.
"""
setup = _SetupBuilder(_SetupBuilder.EXIT, name)
self.net().add_attribute(TaskGroup.LOCAL_SETUP, setup)
return setup
def task_reporter(self, interval_ms=1000, name=None):
"""
Define operations to be executed at every time interval from
task start-up to finish. These operations are guaranteed to
execute at least once after all other operations of the task are
finished.
Example:
with ops.task_reporter(interval_ms=10000):
ops.LogInfo('10s elapsed')
"""
return _ReporterBuilder(interval_ms, net=self.net(), name=name)
def local_reporter(self, interval_ms=1000, name=None):
"""
Similar to task_report, but operations defined within this block
will run repeatedly for as long as any of the tasks in the current
TaskGroup have not finished.
"""
return _ReporterBuilder(interval_ms, name=name)
ops = Operations()
class _ReporterBuilder(NetBuilder):
def __init__(self, interval_ms, net=None, name=None):
NetBuilder.__init__(self, name)
self._net = net
self.interval_ms = interval_ms
def __exit__(self, etype, *args):
if etype is None:
step = core.to_execution_step(self)
step.RunEveryMillis(self.interval_ms)
if self._net:
self._net.add_attribute(Task.REPORT_STEP, step)
else:
TaskGroup.current().report_step(
step, interval_ms=self.interval_ms)
NetBuilder.__exit__(self, etype, *args)
class _SetupBuilder(NetBuilder):
INIT = 'init'
EXIT = 'exit'
def __init__(self, type, name=None):
NetBuilder.__init__(self, name)
self.type = type
def setup(self, net):
if self.type == _SetupBuilder.INIT:
return core.to_execution_step(self)
def exit(self, net):
if self.type == _SetupBuilder.EXIT:
return core.to_execution_step(self)
class _RunOnce(NetBuilder):
def __init__(self, name=None):
NetBuilder.__init__(self, name)
def __exit__(self, etype, *args):
if etype is None and self._stop_blob is not None:
ops.stop()
NetBuilder.__exit__(self, etype, *args)
class _StopGuard(_RunOnce):
def __init__(self, has_stopped_blob=None, name=None):
_RunOnce.__init__(self, name)
self._stopped = has_stopped_blob
self._ran = False
def __enter__(self):
r = _RunOnce.__enter__(self)
self._stopped = ops.Const(True, blob_out=self._stopped)
return r
def __exit__(self, etype, *args):
if etype is None:
self._ran = True
ops.Const(False, blob_out=self._stopped)
_RunOnce.__exit__(self, etype, *args)
def has_stopped(self):
"""
Return a blob that will be set to scalar bool `True` after
this net builder ran, iff it was halted early.
"""
assert self._ran, 'Context not used yet.'
return self._stopped
class _Loop(NetBuilder):
def __init__(self, iters=None, name=None):
NetBuilder.__init__(self, name, _stop_blob_required=True)
if iters is not None:
self._inc = ops.Const(1)
self._iter = ops.Const(0)
self._num_iters = (
iters if isinstance(iters, core.BlobReference)
else ops.Const(iters))
else:
self._num_iters = None
def iter(self):
assert self._num_iters is not None, (
'This loop does not have a number of iterations.')
assert self._iter is not None, (
'iter() must be called from inside the loop context')
return self._iter
def __enter__(self):
builder = NetBuilder.__enter__(self)
if self._num_iters is not None:
ops.stop_if(ops.GE([self._iter, self._num_iters]))
return builder
def __exit__(self, type, *args):
if type is None and self._num_iters is not None:
self.current_net().Add([self._iter, self._inc], [self._iter])
NetBuilder.__exit__(self, type, *args)
class _RunIf(_RunOnce):
def __init__(self, cond_blob=None, name=None, _already_ran=None):
_RunOnce.__init__(self, name)
assert cond_blob or _already_ran
self._is_else = cond_blob is None
if _already_ran is None:
self._else_blob = ops.Not(cond_blob)
self._already_ran = ops.Const(False)
else:
self._already_ran = _already_ran
self._else_blob = _already_ran if cond_blob is None else (
ops.Or([_already_ran, ops.Not(cond_blob)]))
def __enter__(self):
r = _RunOnce.__enter__(self)
ops.stop_if(self._else_blob)
ops.Const(True, blob_out=self._already_ran)
return r
def Elif(self, cond, name=None):
assert not self._is_else, 'Else not allowed for an Else.'
return NetBuilder.current().add(_RunIf(
cond, name=name or self.name, _already_ran=self._already_ran))
def Else(self, name=None):
assert not self._is_else, 'Elif not allowed for an Else.'
return NetBuilder.current().add(
_RunIf(name=name or self.name, _already_ran=self._already_ran))
class _RunIfNet(NetBuilder):
"""
Generates a single net that uses If operator
"""
def __init__(self, cond_blob, name=None):
NetBuilder.__init__(self, name=name, _use_control_ops=True)
assert cond_blob, 'Conditional blob is not specified for an If net'
self._cond_blob = cond_blob
self._then_net = None
self._else_net = None
def add(self, child):
return NetBuilder.add(self, child)
def __exit__(self, type, *args):
if type is None:
_then_nets = self._children
self._reset_children()
self._then_net = NetBuilder.merge_nets(
_then_nets, self._lexical_scope)
if not self._then_net:
self._then_net = core.Net('empty_then_net')
if_net = core.Net(self.name + '/if_net')
add_if_op(if_net, self._cond_blob, self._lexical_scope,
self._then_net, self._else_net)
self._current_net = if_net
self._children = [if_net]
NetBuilder.__exit__(self, type, *args)
class _RunElseNet(NetBuilder):
"""
Else branch for _RunIfNet builder
"""
def __init__(self, name=None):
NetBuilder.__init__(self, name=name, _use_control_ops=True)
parent = NetBuilder.current(required=False)
assert parent and len(parent._children) > 0 and \
isinstance(parent._children[-1], _RunIfNet), \
'Invalid use of Else builder'
self._if_builder = parent._children[-1]
def __exit__(self, type, *args):
if type is None:
_else_nets = self._children
self._reset_children()
self._if_builder._else_net = NetBuilder.merge_nets(
_else_nets, self._lexical_scope)
if self._if_builder._else_net:
if_else_net = core.Net(self.name + '/if_else_net')
add_if_op(
if_else_net,
self._if_builder._cond_blob,
self._lexical_scope,
self._if_builder._then_net,
self._if_builder._else_net)
self._if_builder._current_net = if_else_net
self._if_builder._children = [if_else_net]
NetBuilder.__exit__(self, type, *args)
class _RunWhileNet(NetBuilder):
"""
Generates a single net that uses While operator
"""
def __init__(self, name=None):
NetBuilder.__init__(self, name=name, _use_control_ops=True)
self._cond_builder = None
def __exit__(self, type, *args):
if type is None:
assert self._cond_builder, \
'Condition builder must be specified in While op'
_cond_blob = self._cond_builder._cond_blob
_cond_net = self._cond_builder._cond_net
loop_body = self._children
self._reset_children()
loop_body_net = NetBuilder.merge_nets(
loop_body, self._lexical_scope)
if not loop_body_net:
loop_body_net = core.Net('empty_loop_body_net')
while_net = core.Net(self.name + '/while_net')
add_while_op(while_net, _cond_blob, self._lexical_scope,
loop_body_net, _cond_net)
self._current_net = while_net
self._children = [while_net]
NetBuilder.__exit__(self, type, *args)
class _RunWhileCondition(NetBuilder):
"""
Computes loop's condition, used in the context of WhileNet.
Last operator must have a single scalar boolean output that will be used
as a condition value, no other blobs created in the condition net are
visible outside of it
"""
def __init__(self, name=None):
NetBuilder.__init__(self, name=name, _use_control_ops=True)
parent = NetBuilder.current(required=False)
assert parent and isinstance(parent, _RunWhileNet), \
'Invalid use of loop condition builder'
assert not parent._cond_builder, \
'Multiple loop condition builders specified'
assert len(parent._children) == 0, \
'Condition definition must be specified before the loop\'s body'
parent._cond_builder = self
self._cond_blob = None
self._cond_net = None
def __exit__(self, type, *args):
if type is None:
condition_body = self._children
self._reset_children()
self._cond_net = NetBuilder.merge_nets(
condition_body, self._lexical_scope)
assert self._cond_net, 'Invalid loop condition specified'
assert len(self._cond_net.Proto().op) > 0, 'Invalid condition net'
last_op = self._cond_net.Proto().op[-1]
assert len(last_op.output) == 1, 'Invalid condition net'
self._cond_blob = core.BlobReference(name=last_op.output[0], net=None)
self._current_net = self._cond_net
self._children = [self._cond_net]
NetBuilder.__exit__(self, type, *args)