def gt_region_for_roi(self, target_spec):
if self.mask_distances:
gt_spec = target_spec.copy()
gt_spec.roi = gt_spec.roi.grow(
Coordinate((self.max_distance,) * gt_spec.voxel_size.dims),
Coordinate((self.max_distance,) * gt_spec.voxel_size.dims),
).snap_to_grid(gt_spec.voxel_size, mode="shrink")
else:
gt_spec = target_spec.copy()
return gt_spec
def __init__(self, array_config):
self.name = array_config.name
self._source_array = array_config.source_array_config.array_type(
array_config.source_array_config
)
self.upsample = Coordinate(max(u, 1) for u in array_config.upsample)
self.downsample = Coordinate(max(d, 1) for d in array_config.downsample)
self.interp_order = array_config.interp_order
assert (
self.voxel_size * self.upsample
) / self.downsample == self._source_array.voxel_size
def padding(neighborhood, voxel_size):
"""
Get the appropriate padding to make sure all provided affinities are "True"
"""
dims = voxel_size.dims
padding_neg = (Coordinate(
min([0] + [a[d] for a in neighborhood])
for d in range(dims)) * voxel_size)
padding_pos = (Coordinate(
max([0] + [a[d] for a in neighborhood])
for d in range(dims)) * voxel_size)
return padding_neg, padding_pos
def __getitem__(self, roi: Roi) -> np.ndarray[Any, Any]:
if not self.roi.contains(roi):
raise ValueError(
f"Cannot fetch data from outside my roi: {self.roi}!")
assert roi.offset % self.voxel_size == Coordinate(
(0, ) * self.dims
), f"Given roi offset: {roi.offset} is not a multiple of voxel_size: {self.voxel_size}"
assert roi.shape % self.voxel_size == Coordinate(
(0, ) * self.dims
), f"Given roi shape: {roi.shape} is not a multiple of voxel_size: {self.voxel_size}"
slices = tuple(self._slices(roi))
return self.data[slices]
def gt_region_for_roi(self, target_spec):
gt_spec = target_spec.copy()
pad_neg, pad_pos = aff_padding(self.neighborhood,
target_spec.voxel_size)
if self.lsds:
pad_neg = Coordinate(*[
max(a, b)
for a, b in zip(pad_neg, self.lsd_pad(target_spec.voxel_size))
])
pad_pos = Coordinate(*[
max(a, b)
for a, b in zip(pad_pos, self.lsd_pad(target_spec.voxel_size))
])
gt_spec.roi = gt_spec.roi.grow(pad_neg, pad_pos)
gt_spec.dtype = None
return gt_spec
def __get_output_shape(self, input_shape: Coordinate,
in_channels: int) -> Tuple[int, Coordinate]:
device = torch.device("cpu")
for parameter in self.parameters():
device = parameter.device
break
dummy_data = torch.zeros((1, in_channels) + input_shape, device=device)
with torch.no_grad():
out = self.forward(dummy_data)
return out.shape[1], Coordinate(out.shape[2:])
def register_hooks(converter):
"""Central place to register all conversion hooks with the given
converter."""
#########################
# DaCapo specific hooks #
#########################
# class hierarchies:
register_hierarchy_hooks(converter)
#################
# general hooks #
#################
# path to string and back
converter.register_unstructure_hook(
Path,
lambda o: str(o),
)
converter.register_structure_hook(
Path,
lambda o, _: Path(o),
)
# Coordinate to tuple and back
converter.register_unstructure_hook(
Coordinate,
lambda o: tuple(o),
)
converter.register_structure_hook(
Coordinate,
lambda o, _: Coordinate(o),
)
# Roi to coordinate tuple and back
converter.register_unstructure_hook(
Roi,
lambda o:
(converter.unstructure(o.offset), converter.unstructure(o.shape)),
)
converter.register_structure_hook(
Roi,
lambda o, _: Roi(*o),
)
def voxel_size(self):
return Coordinate(1, 2, 2)
def predict(
model: Model,
raw_array: Array,
prediction_array_identifier: LocalArrayIdentifier,
num_cpu_workers: int = 4,
compute_context: ComputeContext = LocalTorch(),
output_roi: Optional[Roi] = None,
):
# get the model's input and output size
input_voxel_size = Coordinate(raw_array.voxel_size)
output_voxel_size = model.scale(input_voxel_size)
input_shape = Coordinate(model.eval_input_shape)
input_size = input_voxel_size * input_shape
output_size = output_voxel_size * model.compute_output_shape(input_shape)[1]
logger.info(
"Predicting with input size %s, output size %s", input_size, output_size
)
# calculate input and output rois
context = (input_size - output_size) / 2
if output_roi is None:
input_roi = raw_array.roi
output_roi = input_roi.grow(-context, -context)
else:
input_roi = output_roi.grow(context, context)
logger.info("Total input ROI: %s, output ROI: %s", input_roi, output_roi)
# prepare prediction dataset
axes = ["c"] + [axis for axis in raw_array.axes if axis != "c"]
ZarrArray.create_from_array_identifier(
prediction_array_identifier,
axes,
output_roi,
model.num_out_channels,
output_voxel_size,
np.float32,
)
# create gunpowder keys
raw = gp.ArrayKey("RAW")
prediction = gp.ArrayKey("PREDICTION")
# assemble prediction pipeline
# prepare data source
pipeline = DaCapoArraySource(raw_array, raw)
# raw: (c, d, h, w)
pipeline += gp.Pad(raw, Coordinate((None,) * input_voxel_size.dims))
# raw: (c, d, h, w)
pipeline += gp.Unsqueeze([raw])
# raw: (1, c, d, h, w)
gt_padding = (output_size - output_roi.shape) % output_size
prediction_roi = output_roi.grow(gt_padding)
# predict
pipeline += gp_torch.Predict(
model=model,
inputs={"x": raw},
outputs={0: prediction},
array_specs={
prediction: gp.ArraySpec(
roi=prediction_roi, voxel_size=output_voxel_size, dtype=np.float32
)
},
spawn_subprocess=False,
device=str(compute_context.device),
)
# raw: (1, c, d, h, w)
# prediction: (1, [c,] d, h, w)
# prepare writing
pipeline += gp.Squeeze([raw, prediction])
# raw: (c, d, h, w)
# prediction: (c, d, h, w)
# raw: (c, d, h, w)
# prediction: (c, d, h, w)
# write to zarr
pipeline += gp.ZarrWrite(
{prediction: prediction_array_identifier.dataset},
prediction_array_identifier.container.parent,
prediction_array_identifier.container.name,
)
# create reference batch request
ref_request = gp.BatchRequest()
ref_request.add(raw, input_size)
ref_request.add(prediction, output_size)
pipeline += gp.Scan(ref_request)
# build pipeline and predict in complete output ROI
with gp.build(pipeline):
pipeline.request_batch(gp.BatchRequest())
container = zarr.open(prediction_array_identifier.container)
dataset = container[prediction_array_identifier.dataset]
dataset.attrs["axes"] = (
raw_array.axes if "c" in raw_array.axes else ["c"] + raw_array.axes
)
def shape(self) -> Coordinate:
data_shape = self.data.shape
spatial_shape = Coordinate(
[data_shape[self.axes.index(axis)] for axis in self.spatial_axes])
return spatial_shape
def lsd_pad(self, voxel_size):
multiplier = 3 # from AddLocalShapeDescriptor Node in funlib.lsd
padding = Coordinate(self.sigma(voxel_size) * multiplier)
return padding
def padding(self, gt_voxel_size: Coordinate) -> Coordinate:
return Coordinate((self.max_distance,) * gt_voxel_size.dims)
def build_batch_provider(self, datasets, model, task, snapshot_container=None):
input_shape = Coordinate(model.input_shape)
output_shape = Coordinate(model.output_shape)
# get voxel sizes
raw_voxel_size = datasets[0].raw.voxel_size
prediction_voxel_size = model.scale(raw_voxel_size)
# define input and output size:
# switch to world units
input_size = raw_voxel_size * input_shape
output_size = prediction_voxel_size * output_shape
# padding of groundtruth/mask
gt_mask_padding = output_size + task.predictor.padding(prediction_voxel_size)
# define keys:
raw_key = gp.ArrayKey("RAW")
gt_key = gp.ArrayKey("GT")
mask_key = gp.ArrayKey("MASK")
target_key = gp.ArrayKey("TARGET")
weight_key = gp.ArrayKey("WEIGHT")
# Get source nodes
dataset_sources = []
for dataset in datasets:
raw_source = DaCapoArraySource(dataset.raw, raw_key)
raw_source += gp.Pad(raw_key, None, 0)
gt_source = DaCapoArraySource(dataset.gt, gt_key)
gt_source += gp.Pad(gt_key, gt_mask_padding, 0)
if dataset.mask is not None:
mask_source = DaCapoArraySource(dataset.mask, mask_key)
else:
# Always provide a mask. By default it is simply an array
# of ones with the same shape/roi as gt. Avoids making us
# specially handle no mask case and allows padding of the
# ground truth without worrying about training on incorrect
# data.
mask_source = DaCapoArraySource(OnesArray.like(dataset.gt), mask_key)
mask_source += gp.Pad(mask_key, gt_mask_padding, 0)
array_sources = [raw_source, gt_source, mask_source]
dataset_source = (
tuple(array_sources) + gp.MergeProvider() + gp.RandomLocation()
)
dataset_sources.append(dataset_source)
pipeline = tuple(dataset_sources) + gp.RandomProvider()
for augment in self.augments:
pipeline += augment.node(raw_key, gt_key, mask_key)
pipeline += gp.Reject(mask_key, min_masked=self.min_masked)
# Add predictor nodes to pipeline
pipeline += DaCapoTargetFilter(
task.predictor,
gt_key=gt_key,
target_key=target_key,
weights_key=weight_key,
mask_key=mask_key,
)
# Trainer attributes:
if self.num_data_fetchers > 1:
pipeline += gp.PreCache(num_workers=self.num_data_fetchers)
# stack to create a batch dimension
pipeline += gp.Stack(self.batch_size)
# print profiling stats
pipeline += gp.PrintProfilingStats(every=self.print_profiling)
# generate request for all necessary inputs to training
request = gp.BatchRequest()
request.add(raw_key, input_size)
request.add(target_key, output_size)
request.add(weight_key, output_size)
# request additional keys for snapshots
request.add(gt_key, output_size)
request.add(mask_key, output_size)
self._request = request
self._pipeline = pipeline
self._raw_key = raw_key
self._gt_key = gt_key
self._mask_key = mask_key
self._weight_key = weight_key
self._target_key = target_key
self._loss = task.loss
self.snapshot_container = snapshot_container
def eval_shape_increase(self) -> Coordinate:
"""
How much to increase the input shape during prediction.
"""
return Coordinate((0, ) * self.input_shape.dims)
def input_shape(self):
return Coordinate(40, 20, 20)
def padding(self, gt_voxel_size: Coordinate) -> Coordinate:
return Coordinate((0, ) * gt_voxel_size.dims)
def create_from_array_identifier(
cls,
array_identifier,
axes,
roi,
num_channels,
voxel_size,
dtype,
write_size=None,
name=None,
):
"""
Create a new ZarrArray given an array identifier. It is assumed that
this array_identifier points to a dataset that does not yet exist
"""
if write_size is None:
# total storage per block is approx c*x*y*z*dtype_size
# appropriate block size about 5MB.
axis_length = (
(1024**2 * 5 /
(num_channels if num_channels is not None else 1) /
np.dtype(dtype).itemsize)**(1 / voxel_size.dims)) // 1
write_size = Coordinate(
(axis_length, ) * voxel_size.dims) * voxel_size
write_size = Coordinate(
(min(a, b) for a, b in zip(write_size, roi.shape)))
zarr_container = zarr.open(array_identifier.container, "a")
try:
daisy.prepare_ds(
f"{array_identifier.container}",
array_identifier.dataset,
roi,
voxel_size,
dtype,
num_channels=num_channels,
write_size=write_size,
)
zarr_dataset = zarr_container[array_identifier.dataset]
zarr_dataset.attrs["offset"] = roi.offset
zarr_dataset.attrs["resolution"] = voxel_size
zarr_dataset.attrs["axes"] = axes
except zarr.errors.ContainsArrayError:
zarr_dataset = zarr_container[array_identifier.dataset]
assert (tuple(zarr_dataset.attrs["offset"]) == roi.offset
), f"{zarr_dataset.attrs['offset']}, {roi.offset}"
assert (tuple(zarr_dataset.attrs["resolution"]) == voxel_size
), f"{zarr_dataset.attrs['resolution']}, {voxel_size}"
assert tuple(zarr_dataset.attrs["axes"]) == tuple(
axes), f"{zarr_dataset.attrs['axes']}, {axes}"
assert (
zarr_dataset.shape == (
(num_channels, ) if num_channels is not None else
()) + roi.shape / voxel_size
), f"{zarr_dataset.shape}, {((num_channels,) if num_channels is not None else ()) + roi.shape / voxel_size}"
zarr_dataset[:] = np.zeros(zarr_dataset.shape, dtype)
zarr_array = cls.__new__(cls)
zarr_array.file_name = array_identifier.container
zarr_array.dataset = array_identifier.dataset
zarr_array._axes = None
zarr_array._attributes = zarr_array.data.attrs
zarr_array.snap_to_grid = None
return zarr_array
def sigma(self, voxel_size):
voxel_dist = max(voxel_size) # arbitrarily chosen
num_voxels = 10 # arbitrarily chosen
sigma = voxel_dist * num_voxels
return Coordinate((sigma, ) * self.dims)