def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
with self._open_file(self.filename) as data_file:
for (array_key, request_spec) in request.array_specs.items():
voxel_size = self.spec[array_key].voxel_size
# scale request roi to voxel units
dataset_roi = request_spec.roi / voxel_size
# shift request roi into dataset
dataset_roi = dataset_roi - self.spec[
array_key].roi.get_offset() / voxel_size
# create array spec
array_spec = self.spec[array_key].copy()
array_spec.roi = request_spec.roi
# add array to batch
batch.arrays[array_key] = Array(
self.__read(data_file, self.datasets[array_key],
dataset_roi, self.channel_ids[array_key]),
array_spec)
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
report_next_timeout = 2
num_rejected = 0
timing = Timing(self)
timing.start()
have_good_batch = False
while not have_good_batch:
batch = self.upstream_provider.request_batch(request)
if batch.arrays[self.ensure_nonempty].data.size != 0:
have_good_batch = True
logger.debug("Accepted batch with shape: %s",
batch.arrays[self.ensure_nonempty].data.shape)
else:
num_rejected += 1
if timing.elapsed() > report_next_timeout:
logger.info(
"rejected %s batches, been waiting for a good one "
"since %s", num_rejected, report_next_timeout)
report_next_timeout *= 2
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
min_bb = request[self.points].roi.get_begin()
max_bb = request[self.points].roi.get_end()
logger.debug(
"CSV points source got request for %s",
request[self.points].roi)
point_filter = np.ones((self.data.shape[0],), dtype=np.bool)
for d in range(self.ndims):
point_filter = np.logical_and(point_filter, self.data[:,d] >= min_bb[d])
point_filter = np.logical_and(point_filter, self.data[:,d] < max_bb[d])
filtered = self.data[point_filter]
ids = np.arange(len(self.data))[point_filter]
points_data = {
i: Point(p)
for i, p in zip(ids, filtered)
}
points_spec = PointsSpec(roi=request[self.points].roi.copy())
batch = Batch()
batch.points[self.points] = Points(points_data, points_spec)
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
for key, spec in request.items():
logger.debug(f"fetching {key} in roi {spec.roi}")
requested_graph = self.graph_provider.get_graph(
spec.roi,
edge_inclusion="either",
node_inclusion="dangling",
node_attrs=self.node_attrs,
edge_attrs=self.edge_attrs,
nodes_filter=self.nodes_filter,
edges_filter=self.edges_filter,
)
logger.debug(
f"got {len(requested_graph.nodes)} nodes and {len(requested_graph.edges)} edges"
)
for node, attrs in list(requested_graph.nodes.items()):
if self.dist_attribute in attrs:
if attrs[self.dist_attribute] < self.min_dist:
requested_graph.remove_node(node)
logger.debug(
f"{len(requested_graph.nodes)} nodes remaining after filtering by distance"
)
if len(requested_graph.nodes) > self.num_nodes:
nodes = list(requested_graph.nodes)
nodes_to_keep = set(random.sample(nodes, self.num_nodes))
for node in list(requested_graph.nodes()):
if node not in nodes_to_keep:
requested_graph.remove_node(node)
for node, attrs in requested_graph.nodes.items():
attrs["location"] = np.array(attrs[self.position_attribute],
dtype=np.float32)
attrs["id"] = node
if spec.directed:
requested_graph = requested_graph.to_directed()
else:
requested_graph = requested_graph.to_undirected()
logger.debug(
f"providing {key} with {len(requested_graph.nodes)} nodes and {len(requested_graph.edges)} edges"
)
points = Graph.from_nx_graph(requested_graph, spec)
points.crop(spec.roi)
batch[key] = points
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
if request != self.current_request:
if self.workers is not None:
logger.info(
"new request received, stopping current workers...")
self.workers.stop()
self.current_request = copy.deepcopy(request)
logger.info("starting new set of workers...")
self.workers = ProducerPool([
lambda i=i: self.__run_worker(i)
for i in range(self.num_workers)
],
queue_size=self.cache_size)
self.workers.start()
logger.debug("getting batch from queue...")
batch = self.workers.get()
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
_, request_spec = request.array_specs.items()[0]
logger.debug("Reading %s in %s...", self.array, request_spec.roi)
voxel_size = self.spec[self.array].voxel_size
# scale request roi to voxel units
dataset_roi = request_spec.roi/voxel_size
# shift request roi into dataset
dataset_roi = dataset_roi - self.spec[self.array].roi.get_offset()/voxel_size
# create array spec
array_spec = self.spec[self.array].copy()
array_spec.roi = request_spec.roi
# add array to batch
batch.arrays[self.array] = Array(
self.__read(dataset_roi),
array_spec)
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
# operate on a copy of the request, to provide the original request to
# 'process' for convenience
upstream_request = copy.deepcopy(request)
skip = self.__can_skip(request)
timing_prepare = Timing(self, 'prepare')
timing_prepare.start()
if not skip:
self.prepare(upstream_request)
self.remove_provided(upstream_request)
timing_prepare.stop()
batch = self.get_upstream_provider().request_batch(upstream_request)
timing_process = Timing(self, 'process')
timing_process.start()
if not skip:
self.process(batch, request)
timing_process.stop()
batch.profiling_stats.add(timing_prepare)
batch.profiling_stats.add(timing_process)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
min_bb = request[self.points].roi.get_begin()
max_bb = request[self.points].roi.get_end()
logger.debug(
"CSV points source got request for %s",
request[self.points].roi)
point_filter = np.ones((self.locations.shape[0],), dtype=np.bool)
for d in range(self.locations.shape[1]):
point_filter = np.logical_and(point_filter,
self.locations[:, d] >= min_bb[d])
point_filter = np.logical_and(point_filter,
self.locations[:, d] < max_bb[d])
points_data = self._get_points(point_filter)
logger.debug("Points data: %s", points_data)
logger.debug("Type of point: %s", type(list(points_data.values())[0]))
points_spec = PointsSpec(roi=request[self.points].roi.copy())
batch = Batch()
batch.points[self.points] = Points(points_data, points_spec)
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
spec = self.get_spec()
batch = Batch()
with h5py.File(self.filename, 'r') as f:
for (volume_type, roi) in request.volumes.items():
if volume_type not in spec.volumes:
raise RuntimeError("Asked for %s which this source does not provide"%volume_type)
if not spec.volumes[volume_type].contains(roi):
raise RuntimeError("%s's ROI %s outside of my ROI %s"%(volume_type,roi,spec.volumes[volume_type]))
logger.debug("Reading %s in %s..."%(volume_type,roi))
# shift request roi into dataset
dataset_roi = roi.shift(-spec.volumes[volume_type].get_offset())
batch.volumes[volume_type] = Volume(
self.__read(f, self.datasets[volume_type], dataset_roi),
roi=roi,
resolution=self.resolutions[volume_type])
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
min_bb = request[self.points].roi.get_begin()
max_bb = request[self.points].roi.get_end()
logger.debug("CSV points source got request for %s",
request[self.points].roi)
point_filter = np.ones((self.data.shape[0], ), dtype=np.bool)
for d in range(self.ndims):
point_filter = np.logical_and(point_filter,
self.data[:, d] >= min_bb[d])
point_filter = np.logical_and(point_filter,
self.data[:, d] < max_bb[d])
points_data = self._get_points(point_filter)
points_spec = GraphSpec(roi=request[self.points].roi.copy())
batch = Batch()
batch.graphs[self.points] = Graph(points_data, [], points_spec)
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
for key, spec in request.items():
logger.debug(f"fetching {key} in roi {spec.roi}")
requested_graph = self.graph_provider.get_graph(
spec.roi,
edge_inclusion=self.edge_inclusion,
node_inclusion=self.node_inclusion,
node_attrs=self.node_attrs,
edge_attrs=self.edge_attrs,
nodes_filter=self.nodes_filter,
edges_filter=self.edges_filter,
)
logger.debug(
f"got {len(requested_graph.nodes)} nodes and {len(requested_graph.edges)} edges"
)
failed_nodes = []
for node, attrs in requested_graph.nodes.items():
try:
attrs["location"] = np.array(
attrs[self.position_attribute], dtype=np.float32)
except KeyError:
logger.warning(
f"node: {node} was written (probably part of an edge), but never given coordinates!"
)
failed_nodes.append(node)
attrs["id"] = node
for node in failed_nodes:
if self.fail_on_inconsistent_node:
raise ValueError(
f"Mongodb contains node {node} without location! "
f"It was probably written as part of an edge")
requested_graph.remove_node(node)
if spec.directed:
requested_graph = requested_graph.to_directed()
else:
requested_graph = requested_graph.to_undirected()
points = Graph.from_nx_graph(requested_graph, spec)
points.relabel_connected_components()
points.crop(spec.roi)
batch[key] = points
logger.debug(f"{key} with {len(list(points.nodes))} nodes")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
skip = self.__can_skip(request)
timing_prepare = Timing(self, "prepare")
timing_prepare.start()
downstream_request = request.copy()
if not skip:
dependencies = self.prepare(request)
if isinstance(dependencies, BatchRequest):
upstream_request = request.merge(dependencies)
elif dependencies is None:
upstream_request = request.copy()
else:
raise BatchFilterError(
self, f"This BatchFilter returned a {type(dependencies)}! "
"Supported return types are: `BatchRequest` containing your exact "
"dependencies or `None`, indicating a dependency on the full request."
)
self.remove_provided(upstream_request)
else:
upstream_request = request.copy()
self.remove_provided(upstream_request)
timing_prepare.stop()
batch = self.get_upstream_provider().request_batch(upstream_request)
timing_process = Timing(self, "process")
timing_process.start()
if not skip:
if dependencies is not None:
dependencies.remove_placeholders()
node_batch = batch.crop(dependencies)
else:
node_batch = batch
downstream_request.remove_placeholders()
processed_batch = self.process(node_batch, downstream_request)
if processed_batch is None:
processed_batch = node_batch
batch = batch.merge(
processed_batch,
merge_profiling_stats=False).crop(downstream_request)
timing_process.stop()
batch.profiling_stats.add(timing_prepare)
batch.profiling_stats.add(timing_process)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
logger.debug("getting batch from queue...")
batch = self.workers.get()
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request: BatchRequest) -> Batch:
timing = Timing(self, "provide")
timing.start()
batch = Batch()
for points_key in self.points:
if points_key not in request:
continue
# Retrieve all points in the requested region using a kdtree for speed
point_ids = self._query_kdtree(
self.data.tree,
(
np.array(request[points_key].roi.get_begin()),
np.array(request[points_key].roi.get_end()),
),
)
# To account for boundary crossings we must retrieve neighbors of all points
# in the graph. This is too slow for large queries and less important
points_subgraph = self._subgraph_points(
point_ids,
with_neighbors=len(point_ids) < len(self._graph.nodes) // 2)
nodes = [
Node(id=node, location=attrs["location"], attrs=attrs)
for node, attrs in points_subgraph.nodes.items()
]
edges = [Edge(u, v) for u, v in points_subgraph.edges]
return_graph = Graph(nodes, edges,
GraphSpec(roi=request[points_key].roi))
# Handle boundary cases
return_graph = return_graph.trim(request[points_key].roi)
batch = Batch()
batch.points[points_key] = return_graph
logger.debug(
"Graph points source provided {} points for roi: {}".format(
len(list(batch.points[points_key].nodes)),
request[points_key].roi))
logger.debug(
f"Providing {len(list(points_subgraph.nodes))} nodes to {points_key}"
)
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing_process = Timing(self)
timing_process.start()
batch = Batch()
with h5py.File(self.filename, 'r') as hdf_file:
# if pre and postsynaptic locations required, their id
# SynapseLocation dictionaries should be created together s.t. ids
# are unique and allow to find partner locations
if PointsKeys.PRESYN in request.points_specs or PointsKeys.POSTSYN in request.points_specs:
assert self.kind == 'synapse'
# If only PRESYN or POSTSYN requested, assume PRESYN ROI = POSTSYN ROI.
pre_key = PointsKeys.PRESYN if PointsKeys.PRESYN in request.points_specs else PointsKeys.POSTSYN
post_key = PointsKeys.POSTSYN if PointsKeys.POSTSYN in request.points_specs else PointsKeys.PRESYN
presyn_points, postsyn_points = self.__get_syn_points(
pre_roi=request.points_specs[pre_key].roi,
post_roi=request.points_specs[post_key].roi,
syn_file=hdf_file)
points = {
PointsKeys.PRESYN: presyn_points,
PointsKeys.POSTSYN: postsyn_points
}
else:
assert self.kind == 'presyn' or self.kind == 'postsyn'
synkey = list(self.datasets.items())[0][0] # only key of dic.
presyn_points, postsyn_points = self.__get_syn_points(
pre_roi=request.points_specs[synkey].roi,
post_roi=request.points_specs[synkey].roi,
syn_file=hdf_file)
points = {
synkey:
presyn_points if self.kind == 'presyn' else postsyn_points
}
for (points_key, request_spec) in request.points_specs.items():
logger.debug("Reading %s in %s...", points_key,
request_spec.roi)
points_spec = self.spec[points_key].copy()
points_spec.roi = request_spec.roi
logger.debug("Number of points len()".format(
len(points[points_key])))
batch.points[points_key] = Points(data=points[points_key],
spec=points_spec)
timing_process.stop()
batch.profiling_stats.add(timing_process)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
# if pre and postsynaptic locations requested, their id : SynapseLocation dictionaries should be created
# together s.t. the ids are unique and allow to find partner locations
if GraphKey.PRESYN in request.points or GraphKey.POSTSYN in request.points:
try: # either both have the same roi, or only one of them is requested
assert request.points[GraphKey.PRESYN] == request.points[
GraphKey.POSTSYN]
except AssertionError:
assert GraphKey.PRESYN not in request.points or GraphKey.POSTSYN not in request.points
if GraphKey.PRESYN in request.points:
presyn_points, postsyn_points = self.__read_syn_points(
roi=request.points[GraphKey.PRESYN])
elif GraphKey.POSTSYN in request.points:
presyn_points, postsyn_points = self.__read_syn_points(
roi=request.points[GraphKey.POSTSYN])
for (points_key, roi) in request.points.items():
# check if requested points can be provided
if points_key not in self.spec:
raise RuntimeError(
"Asked for %s which this source does not provide" %
points_key)
# check if request roi lies within provided roi
if not self.spec[points_key].roi.contains(roi):
raise RuntimeError(
"%s's ROI %s outside of my ROI %s" %
(points_key, roi, self.spec[points_key].roi))
logger.debug("Reading %s in %s..." % (points_key, roi))
id_to_point = {
GraphKey.PRESYN: presyn_points,
GraphKey.POSTSYN: postsyn_points
}[points_key]
batch.points[points_key] = Graph(data=id_to_point,
spec=GraphSpec(roi=roi))
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = gp.Batch()
# If a Array is requested then we will randomly choose
# the number of requested points
if isinstance(self.points, gp.ArrayKey):
points = np.random.choice(self.data.shape[0], self.num_points)
data = self.data[points][np.newaxis]
if self.scale is not None:
data = data * self.scale
if self.label_data is not None:
labels = self.label_data[points]
batch[self.points] = gp.Array(data, self.spec[self.points])
else:
# If a graph is request we must select points within the
# request ROI
min_bb = request[self.points].roi.get_begin()
max_bb = request[self.points].roi.get_end()
logger.debug("Points source got request for %s",
request[self.points].roi)
point_filter = np.ones((self.data.shape[0], ), dtype=np.bool)
for d in range(self.ndims):
point_filter = np.logical_and(point_filter,
self.data[:, d] >= min_bb[d])
point_filter = np.logical_and(point_filter,
self.data[:, d] < max_bb[d])
points_data, labels = self._get_points(point_filter)
logger.debug(f"Found {len(points_data)} points")
points_spec = gp.GraphSpec(roi=request[self.points].roi.copy())
batch.graphs[self.points] = gp.Graph(points_data, [], points_spec)
# Labels will always be an Array
if self.label_data is not None:
batch[self.labels] = gp.Array(labels, self.spec[self.labels])
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request: BatchRequest) -> Batch:
"""
First request points with specific seeds, then request the rest if
valid points are found.
"""
logger.debug(f"growing request by {self._get_growth()}")
has_component = False
while not has_component:
base_seed, add_seed, direction, prepare_profiling_stats = self.get_valid_seeds(
request)
timing_prepare = Timing(self, "prepare")
timing_prepare.start()
request_base = self.prepare(request, base_seed, -direction)
if add_seed is not None:
request_add = self.prepare(request, add_seed, direction)
else:
request_add = None
logger.debug(f"No add_request needed!")
timing_prepare.stop()
base = self.upstream_provider.request_batch(request_base)
if request_add is not None:
add = self.upstream_provider.request_batch(request_add)
else:
add = self._empty_copy(base)
has_component = True
timing_process = Timing(self, "process")
timing_process.start()
base = self.process(base, Coordinate([0, 0, 0]), request=request)
add = self.process(add, -Coordinate([0, 0, 0]), request=request)
batch = self.merge_batches(base, add)
timing_process.stop()
batch.profiling_stats.merge_with(prepare_profiling_stats)
batch.profiling_stats.add(timing_prepare)
batch.profiling_stats.add(timing_process)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
with h5py.File(self.filename, 'r') as hdf_file:
# if pre and postsynaptic locations required, their id
# SynapseLocation dictionaries should be created together s.t. ids
# are unique and allow to find partner locations
if PointsKeys.PRESYN in request.points_specs or PointsKeys.POSTSYN in request.points_specs:
assert request.points_specs[
PointsKeys.PRESYN].roi == request.points_specs[
PointsKeys.POSTSYN].roi
# Cremi specific, ROI offset corresponds to offset present in the
# synapse location relative to the raw data.
dataset_offset = self.spec[PointsKeys.PRESYN].roi.get_offset()
presyn_points, postsyn_points = self.__get_syn_points(
roi=request.points_specs[PointsKeys.PRESYN].roi,
syn_file=hdf_file,
dataset_offset=dataset_offset)
for (points_key, request_spec) in request.points_specs.items():
logger.debug("Reading %s in %s...", points_key,
request_spec.roi)
id_to_point = {
PointsKeys.PRESYN: presyn_points,
PointsKeys.POSTSYN: postsyn_points
}[points_key]
points_spec = self.spec[points_key].copy()
points_spec.roi = request_spec.roi
batch.points[points_key] = Points(data=id_to_point,
spec=points_spec)
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
for (array_key, request_spec) in request.array_specs.items():
logger.debug("Reading %s in %s...", array_key, request_spec.roi)
voxel_size = self.spec[array_key].voxel_size
# scale request roi to voxel units
dataset_roi = request_spec.roi / voxel_size
# shift request roi into dataset
dataset_roi = dataset_roi - self.spec[array_key].roi.get_offset(
) / voxel_size
# create array spec
array_spec = self.spec[array_key].copy()
array_spec.roi = request_spec.roi
# read the data
if array_key in self.datasets:
data = self.__read_array(self.datasets[array_key], dataset_roi)
elif array_key in self.masks:
data = self.__read_mask(self.masks[array_key], dataset_roi)
else:
assert False, (
"Encountered a request for %s that is neither a volume "
"nor a mask." % array_key)
# add array to batch
batch.arrays[array_key] = Array(data, array_spec)
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
spec = self.get_spec()
batch = Batch()
with h5py.File(self.filename, 'r') as f:
for (volume_type, roi) in request.volumes.items():
if volume_type not in spec.volumes:
raise RuntimeError(
"Asked for %s which this source does not provide" %
volume_type)
if not spec.volumes[volume_type].contains(roi):
raise RuntimeError(
"%s's ROI %s outside of my ROI %s" %
(volume_type, roi, spec.volumes[volume_type]))
interpolate = {
VolumeType.RAW: True,
VolumeType.GT_LABELS: False,
VolumeType.GT_MASK: False,
VolumeType.ALPHA_MASK: True,
}[volume_type]
logger.debug("Reading %s in %s..." % (volume_type, roi))
batch.volumes[volume_type] = Volume(
self.__read(f, self.datasets[volume_type], roi),
roi=roi,
resolution=self.resolutions[volume_type],
interpolate=interpolate)
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
spec = self.get_spec()
batch = Batch()
logger.debug("providing batch with resolution of {}".format(
self.resolution))
for (volume_type, roi) in request.volumes.items():
if volume_type not in spec.volumes:
raise RuntimeError(
"Asked for %s which this source does not provide" %
volume_type)
if not spec.volumes[volume_type].contains(roi):
raise RuntimeError(
"%s's ROI %s outside of my ROI %s" %
(volume_type, roi, spec.volumes[volume_type]))
read, interpolate = {
VolumeType.RAW: (self.__read_raw, True),
VolumeType.GT_LABELS: (self.__read_gt, False),
VolumeType.GT_MASK: (self.__read_gt_mask, False),
}[volume_type]
logger.debug("Reading %s in %s..." % (volume_type, roi))
batch.volumes[volume_type] = Volume(
read(roi),
roi=roi,
# TODO: get resolution from repository
resolution=self.resolution,
interpolate=interpolate)
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
report_next_timeout = 10
num_rejected = 0
timing = Timing(self)
timing.start()
assert self.mask in request, (
"Reject can only be used if a GT mask is requested")
have_good_batch = False
while not have_good_batch:
batch = self.upstream_provider.request_batch(request)
mask_ratio = batch.arrays[self.mask].data.mean()
have_good_batch = mask_ratio > self.min_masked
if not have_good_batch and self.reject_probability < 1.:
have_good_batch = random.random() > self.reject_probability
if not have_good_batch:
logger.debug("reject batch with mask ratio %f at %s",
mask_ratio, batch.arrays[self.mask].spec.roi)
num_rejected += 1
if timing.elapsed() > report_next_timeout:
logger.warning(
"rejected %d batches, been waiting for a good one "
"since %ds", num_rejected, report_next_timeout)
report_next_timeout *= 2
else:
logger.debug("accepted batch with mask ratio %f at %s",
mask_ratio, batch.arrays[self.mask].spec.roi)
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request: BatchRequest) -> Batch:
random.seed(request.random_seed)
np.random.seed(request.random_seed)
timing = Timing(self, "provide")
timing.start()
batch = Batch()
roi = request[self.points].roi
region_shape = roi.get_shape()
trees = []
for _ in range(self.n_obj):
for _ in range(100):
root = np.random.random(len(region_shape)) * region_shape
tree = self._grow_tree(
root, Roi((0,) * len(region_shape), region_shape)
)
if self.num_nodes[0] <= len(tree.nodes) <= self.num_nodes[1]:
break
trees.append(tree)
# logger.info("{} trees got, expected {}".format(len(trees), self.n_obj))
trees_graph = nx.disjoint_union_all(trees)
points = {
node_id: Node(np.floor(node_attrs["pos"]) + roi.get_begin())
for node_id, node_attrs in trees_graph.nodes.items()
}
batch[self.points] = Graph(points, request[self.points], list(trees_graph.edges))
timing.stop()
batch.profiling_stats.add(timing)
# self._plot_tree(tree)
return batch
def provide(self, request):
# operate on a copy of the request, to provide the original request to
# 'process' for convenience
upstream_request = copy.deepcopy(request)
timing = Timing(self)
timing.start()
self.prepare(upstream_request)
timing.stop()
batch = self.get_upstream_provider().request_batch(upstream_request)
timing.start()
self.process(batch, request)
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
# update recent requests
self.last_5.popleft()
self.last_5.append(request)
if request != self.current_request:
current_count = sum([
recent_request == self.current_request
for recent_request in self.last_5
])
new_count = sum(
[recent_request == request for recent_request in self.last_5])
if new_count > current_count or self.current_request is None:
if self.workers is not None:
logger.info(
"new request received, stopping current workers...")
self.workers.stop()
self.current_request = copy.deepcopy(request)
logger.info(
"starting new set of workers (%s, cache size %s)...",
self.num_workers, self.cache_size)
self.workers = ProducerPool(
[
lambda i=i: self.__run_worker(i)
for i in range(self.num_workers)
],
queue_size=self.cache_size,
)
self.workers.start()
logger.debug("getting batch from queue...")
batch = self.workers.get()
timing.stop()
batch.profiling_stats.add(timing)
else:
logger.debug("Resolving new request sequentially")
batch = self.get_upstream_provider().request_batch(request)
timing.stop()
batch.profiling_stats.add(timing)
else:
logger.debug("getting batch from queue...")
batch = self.workers.get()
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
output = gp.Batch()
timing_provide = Timing(self, "provide")
timing_provide.start()
spec = self.array_spec.copy()
spec.roi = request[self.key].roi
data = self.array[spec.roi]
if "c" not in self.array.axes:
# add a channel dimension
data = np.expand_dims(data, 0)
if np.any(np.isnan(data)):
raise ValueError("INPUT DATA CAN'T BE NAN")
output[self.key] = gp.Array(data, spec=spec)
timing_provide.stop()
output.profiling_stats.add(timing_provide)
return output
def provide(self, request):
report_next_timeout = 10
num_rejected = 0
timing = Timing(self)
timing.start()
assert self.mask_volume_type in request.volumes, "Reject can only be used if a GT mask is requested"
have_good_batch = False
while not have_good_batch:
batch = self.upstream_provider.request_batch(request)
mask_ratio = batch.volumes[self.mask_volume_type].data.mean()
have_good_batch = mask_ratio >= self.min_masked
if not have_good_batch:
logger.debug("reject batch with mask ratio %f at " %
mask_ratio +
str(batch.volumes[self.mask_volume_type].roi))
num_rejected += 1
if timing.elapsed() > report_next_timeout:
logger.warning(
"rejected %d batches, been waiting for a good one since %ds"
% (num_rejected, report_next_timeout))
report_next_timeout *= 2
logger.debug("good batch with mask ratio %f found at " % mask_ratio +
str(batch.volumes[self.mask_volume_type].roi))
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
with h5py.File(self.filename, 'r') as hdf_file:
for (array_key, request_spec) in request.array_specs.items():
logger.debug("Reading %s in %s...", array_key,
request_spec.roi)
voxel_size = self.spec[array_key].voxel_size
# scale request roi to voxel units
dataset_roi = request_spec.roi / voxel_size
# shift request roi into dataset
dataset_roi = dataset_roi - self.spec[
array_key].roi.get_offset() / voxel_size
# create array spec
array_spec = self.spec[array_key].copy()
array_spec.roi = request_spec.roi
# add array to batch
batch.arrays[array_key] = Array(
self.__read(hdf_file, self.datasets[array_key],
dataset_roi), array_spec)
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
cv = CloudVolume(self.cloudvolume_url, use_https=True, mip=self.mip)
request_spec = request.array_specs[self.array_key]
array_key = self.array_key
logger.debug("Reading %s in %s...", array_key, request_spec.roi)
voxel_size = self.array_spec.voxel_size
# scale request roi to voxel units
dataset_roi = request_spec.roi / voxel_size
# shift request roi into dataset
dataset_roi = dataset_roi - self.spec[
array_key].roi.get_offset() / voxel_size
# create array spec
array_spec = self.array_spec.copy()
array_spec.roi = request_spec.roi
# array_spec.voxel_size = array_spec.voxel_size
# add array to batch
batch.arrays[array_key] = Array(
self.__read(cv, dataset_roi),
array_spec)
logger.debug("done")
timing.stop()
batch.profiling_stats.add(timing)
return batch