def test_write_progress_to_file(tmpdir):
"""Tests the WriteProgressToFile progress bar type"""
def check_file(filename, startstring):
with open(filename, "r") as fh:
data = fh.read()
assert data.startswith(startstring)
filename = str(tmpdir.join("test_write_progress_file.txt"))
progress = Progress(name_during="myprog", max_steps=2)
bar = WriteProgressToFile(filename)
with progress:
bar.update(progress)
check_file(filename, "0%, ETA")
progress.step()
bar.update(progress)
check_file(filename, "50%, ETA")
progress.step()
bar.update(progress)
check_file(filename, "100%, ETA")
bar.update(progress)
check_file(filename, "myprog finished in")
def build_network(model, network, progress=None):
"""Builds a `.Network` object into a model.
The network builder does this by mapping each high-level object to its
associated signals and operators one-by-one, in the following order:
1. Ensembles, nodes, neurons
2. Subnetworks (recursively)
3. Connections, learning rules
4. Probes
Before calling any of the individual objects' build functions, random
number seeds are assigned to objects that did not have a seed explicitly
set by the user. Whether the seed was assigned manually or automatically
is tracked, and the decoder cache is only used when the seed is assigned
manually.
Parameters
----------
model : Model
The model to build into.
network : Network
The network to build.
progress : Progress, optional
Object used to track the build progress.
Note that this will only affect top-level networks.
Notes
-----
Sets ``model.params[network]`` to ``None``.
"""
if model.toplevel is None:
model.toplevel = network
seed_network(network, seeds=model.seeds, seeded=model.seeded)
if progress is not None:
# number of sub-objects, plus 1 to account for this network
progress.max_steps = len(network.all_objects) + 1
def build_callback(obj):
if isinstance(obj, tuple(network.objects)):
progress.step()
model.build_callback = build_callback
if progress is None:
progress = Progress() # dummy progress
# Set config
old_config = model.config
model.config = network.config
# If this is the toplevel network, enter the decoder cache
context = model.decoder_cache if model.toplevel is network else nullcontext(
)
with context, progress:
logger.debug("Network step 1: Building ensembles and nodes")
for obj in network.ensembles + network.nodes:
model.build(obj)
logger.debug("Network step 2: Building subnetworks")
for subnetwork in network.networks:
model.build(subnetwork)
logger.debug("Network step 3: Building connections")
for conn in network.connections:
# NB: we do these in the order in which they're defined, and build
# the learning rule in the connection builder. Because learning
# rules are attached to connections, the connection that contains
# the learning rule (and the learning rule) are always built
# *before* a connection that attaches to that learning rule.
# Therefore, we don't have to worry about connection ordering here.
# TODO: Except perhaps if the connection being learned
# is in a subnetwork?
model.build(conn)
logger.debug("Network step 4: Building probes")
for probe in network.probes:
model.build(probe)
if context is model.decoder_cache:
model.decoder_cache.shrink()
if model.toplevel is network:
progress.step()
model.build_callback = None
# Unset config
model.config = old_config
model.params[network] = None
def build_network(model, network, progress=None):
"""Builds a `.Network` object into a model.
The network builder does this by mapping each high-level object to its
associated signals and operators one-by-one, in the following order:
1. Ensembles, nodes, neurons
2. Subnetworks (recursively)
3. Connections, learning rules
4. Probes
Before calling any of the individual objects' build functions, random
number seeds are assigned to objects that did not have a seed explicitly
set by the user. Whether the seed was assigned manually or automatically
is tracked, and the decoder cache is only used when the seed is assigned
manually.
Parameters
----------
model : Model
The model to build into.
network : Network
The network to build.
progress : Progress, optional
Object used to track the build progress.
Note that this will only affect top-level networks.
Notes
-----
Sets ``model.params[network]`` to ``None``.
"""
def get_seed(obj, rng):
# Generate a seed no matter what, so that setting a seed or not on
# one object doesn't affect the seeds of other objects.
seed = rng.randint(npext.maxint)
return (seed
if not hasattr(obj, 'seed') or obj.seed is None else obj.seed)
if model.toplevel is None:
model.toplevel = network
model.seeds[network] = get_seed(network, np.random)
model.seeded[network] = getattr(network, 'seed', None) is not None
max_steps = len(network.all_objects) + 1 # +1 for top level network
if progress is not None:
progress.max_steps = max_steps
def build_callback(obj):
if isinstance(obj, tuple(network.objects)):
progress.step()
model.build_callback = build_callback
if progress is None:
progress = Progress() # dummy progress
# Set config
old_config = model.config
model.config = network.config
# assign seeds to children
rng = np.random.RandomState(model.seeds[network])
# Put probes last so that they don't influence other seeds
sorted_types = (Connection, Ensemble, Network, Node, Probe)
assert all(tp in sorted_types for tp in network.objects)
for obj_type in sorted_types:
for obj in network.objects[obj_type]:
model.seeded[obj] = (model.seeded[network]
or getattr(obj, 'seed', None) is not None)
model.seeds[obj] = get_seed(obj, rng)
# If this is the toplevel network, enter the decoder cache
context = (model.decoder_cache
if model.toplevel is network else nullcontext())
with context, progress:
logger.debug("Network step 1: Building ensembles and nodes")
for obj in network.ensembles + network.nodes:
model.build(obj)
logger.debug("Network step 2: Building subnetworks")
for subnetwork in network.networks:
model.build(subnetwork)
logger.debug("Network step 3: Building connections")
for conn in network.connections:
# NB: we do these in the order in which they're defined, and build
# the learning rule in the connection builder. Because learning
# rules are attached to connections, the connection that contains
# the learning rule (and the learning rule) are always built
# *before* a connection that attaches to that learning rule.
# Therefore, we don't have to worry about connection ordering here.
# TODO: Except perhaps if the connection being learned
# is in a subnetwork?
model.build(conn)
logger.debug("Network step 4: Building probes")
for probe in network.probes:
model.build(probe)
if context is model.decoder_cache:
model.decoder_cache.shrink()
if model.toplevel is network:
progress.step()
model.build_callback = None
# Unset config
model.config = old_config
model.params[network] = None
