Coverage for src/bioimageio/core/digest_spec.py: 79%
215 statements
« prev ^ index » next coverage.py v7.10.7, created at 2025-09-22 09:21 +0000
1from __future__ import annotations
3import collections.abc
4import importlib.util
5import sys
6from itertools import chain
7from pathlib import Path
8from tempfile import TemporaryDirectory
9from typing import (
10 Any,
11 Callable,
12 Dict,
13 Iterable,
14 List,
15 Mapping,
16 NamedTuple,
17 Optional,
18 Sequence,
19 Tuple,
20 Union,
21)
22from zipfile import ZipFile, is_zipfile
24import numpy as np
25import xarray as xr
26from loguru import logger
27from numpy.typing import NDArray
28from typing_extensions import Unpack, assert_never
30from bioimageio.spec._internal.io import HashKwargs
31from bioimageio.spec.common import FileDescr, FileSource, ZipPath
32from bioimageio.spec.model import AnyModelDescr, v0_4, v0_5
33from bioimageio.spec.model.v0_4 import CallableFromDepencency, CallableFromFile
34from bioimageio.spec.model.v0_5 import (
35 ArchitectureFromFileDescr,
36 ArchitectureFromLibraryDescr,
37 ParameterizedSize_N,
38)
39from bioimageio.spec.utils import load_array
41from .axis import Axis, AxisId, AxisInfo, AxisLike, PerAxis
42from .block_meta import split_multiple_shapes_into_blocks
43from .common import Halo, MemberId, PerMember, SampleId, TotalNumberOfBlocks
44from .io import load_tensor
45from .sample import (
46 LinearSampleAxisTransform,
47 Sample,
48 SampleBlockMeta,
49 sample_block_meta_generator,
50)
51from .stat_measures import Stat
52from .tensor import Tensor
54TensorSource = Union[Tensor, xr.DataArray, NDArray[Any], Path]
57def import_callable(
58 node: Union[
59 ArchitectureFromFileDescr,
60 ArchitectureFromLibraryDescr,
61 CallableFromDepencency,
62 CallableFromFile,
63 ],
64 /,
65 **kwargs: Unpack[HashKwargs],
66) -> Callable[..., Any]:
67 """import a callable (e.g. a torch.nn.Module) from a spec node describing it"""
68 if isinstance(node, CallableFromDepencency):
69 module = importlib.import_module(node.module_name)
70 c = getattr(module, str(node.callable_name))
71 elif isinstance(node, ArchitectureFromLibraryDescr):
72 module = importlib.import_module(node.import_from)
73 c = getattr(module, str(node.callable))
74 elif isinstance(node, CallableFromFile):
75 c = _import_from_file_impl(node.source_file, str(node.callable_name), **kwargs)
76 elif isinstance(node, ArchitectureFromFileDescr):
77 c = _import_from_file_impl(node.source, str(node.callable), sha256=node.sha256)
78 else:
79 assert_never(node)
81 if not callable(c):
82 raise ValueError(f"{node} (imported: {c}) is not callable")
84 return c
87tmp_dirs_in_use: List[TemporaryDirectory[str]] = []
88"""keep global reference to temporary directories created during import to delay cleanup"""
91def _import_from_file_impl(
92 source: FileSource, callable_name: str, **kwargs: Unpack[HashKwargs]
93):
94 src_descr = FileDescr(source=source, **kwargs)
95 # ensure sha is valid even if perform_io_checks=False
96 # or the source has changed since last sha computation
97 src_descr.validate_sha256(force_recompute=True)
98 assert src_descr.sha256 is not None
99 source_sha = src_descr.sha256
101 reader = src_descr.get_reader()
102 # make sure we have unique module name
103 module_name = f"{reader.original_file_name.split('.')[0]}_{source_sha}"
105 # make sure we have a unique and valid module name
106 if not module_name.isidentifier():
107 module_name = f"custom_module_{source_sha}"
108 assert module_name.isidentifier(), module_name
110 source_bytes = reader.read()
112 module = sys.modules.get(module_name)
113 if module is None:
114 try:
115 td_kwargs: Dict[str, Any] = (
116 dict(ignore_cleanup_errors=True) if sys.version_info >= (3, 10) else {}
117 )
118 if sys.version_info >= (3, 12):
119 td_kwargs["delete"] = False
121 tmp_dir = TemporaryDirectory(**td_kwargs)
122 # keep global ref to tmp_dir to delay cleanup until program exit
123 # TODO: remove for py >= 3.12, when delete=False works
124 tmp_dirs_in_use.append(tmp_dir)
126 module_path = Path(tmp_dir.name) / module_name
127 if reader.original_file_name.endswith(".zip") or is_zipfile(reader):
128 module_path.mkdir()
129 ZipFile(reader).extractall(path=module_path)
130 else:
131 module_path = module_path.with_suffix(".py")
132 _ = module_path.write_bytes(source_bytes)
134 importlib_spec = importlib.util.spec_from_file_location(
135 module_name, str(module_path)
136 )
138 if importlib_spec is None:
139 raise ImportError(f"Failed to import {source}")
141 module = importlib.util.module_from_spec(importlib_spec)
143 sys.modules[module_name] = module # cache this module
145 assert importlib_spec.loader is not None
146 importlib_spec.loader.exec_module(module)
148 except Exception as e:
149 del sys.modules[module_name]
150 raise ImportError(f"Failed to import {source}") from e
152 try:
153 callable_attr = getattr(module, callable_name)
154 except AttributeError as e:
155 raise AttributeError(
156 f"Imported custom module from {source} has no `{callable_name}` attribute."
157 ) from e
158 except Exception as e:
159 raise AttributeError(
160 f"Failed to access `{callable_name}` attribute from custom module imported from {source} ."
161 ) from e
163 else:
164 return callable_attr
167def get_axes_infos(
168 io_descr: Union[
169 v0_4.InputTensorDescr,
170 v0_4.OutputTensorDescr,
171 v0_5.InputTensorDescr,
172 v0_5.OutputTensorDescr,
173 ],
174) -> List[AxisInfo]:
175 """get a unified, simplified axis representation from spec axes"""
176 ret: List[AxisInfo] = []
177 for a in io_descr.axes:
178 if isinstance(a, v0_5.AxisBase):
179 ret.append(AxisInfo.create(Axis(id=a.id, type=a.type)))
180 else:
181 assert a in ("b", "i", "t", "c", "z", "y", "x")
182 ret.append(AxisInfo.create(a))
184 return ret
187def get_member_id(
188 tensor_description: Union[
189 v0_4.InputTensorDescr,
190 v0_4.OutputTensorDescr,
191 v0_5.InputTensorDescr,
192 v0_5.OutputTensorDescr,
193 ],
194) -> MemberId:
195 """get the normalized tensor ID, usable as a sample member ID"""
197 if isinstance(tensor_description, (v0_4.InputTensorDescr, v0_4.OutputTensorDescr)):
198 return MemberId(tensor_description.name)
199 elif isinstance(
200 tensor_description, (v0_5.InputTensorDescr, v0_5.OutputTensorDescr)
201 ):
202 return tensor_description.id
203 else:
204 assert_never(tensor_description)
207def get_member_ids(
208 tensor_descriptions: Sequence[
209 Union[
210 v0_4.InputTensorDescr,
211 v0_4.OutputTensorDescr,
212 v0_5.InputTensorDescr,
213 v0_5.OutputTensorDescr,
214 ]
215 ],
216) -> List[MemberId]:
217 """get normalized tensor IDs to be used as sample member IDs"""
218 return [get_member_id(descr) for descr in tensor_descriptions]
221def get_test_input_sample(model: AnyModelDescr) -> Sample:
222 return _get_test_sample(
223 model.inputs,
224 model.test_inputs if isinstance(model, v0_4.ModelDescr) else model.inputs,
225 )
228get_test_inputs = get_test_input_sample
229"""DEPRECATED: use `get_test_input_sample` instead"""
232def get_test_output_sample(model: AnyModelDescr) -> Sample:
233 """returns a model's test output sample"""
234 return _get_test_sample(
235 model.outputs,
236 model.test_outputs if isinstance(model, v0_4.ModelDescr) else model.outputs,
237 )
240get_test_outputs = get_test_output_sample
241"""DEPRECATED: use `get_test_input_sample` instead"""
244def _get_test_sample(
245 tensor_descrs: Sequence[
246 Union[
247 v0_4.InputTensorDescr,
248 v0_4.OutputTensorDescr,
249 v0_5.InputTensorDescr,
250 v0_5.OutputTensorDescr,
251 ]
252 ],
253 test_sources: Sequence[Union[FileSource, v0_5.TensorDescr]],
254) -> Sample:
255 """returns a model's input/output test sample"""
256 member_ids = get_member_ids(tensor_descrs)
257 arrays: List[NDArray[Any]] = []
258 for src in test_sources:
259 if isinstance(src, (v0_5.InputTensorDescr, v0_5.OutputTensorDescr)):
260 if src.test_tensor is None:
261 raise ValueError(
262 f"Model input '{src.id}' has no test tensor defined, cannot create test sample."
263 )
264 arrays.append(load_array(src.test_tensor))
265 else:
266 arrays.append(load_array(src))
268 axes = [get_axes_infos(t) for t in tensor_descrs]
269 return Sample(
270 members={
271 m: Tensor.from_numpy(arr, dims=ax)
272 for m, arr, ax in zip(member_ids, arrays, axes)
273 },
274 stat={},
275 id="test-sample",
276 )
279class IO_SampleBlockMeta(NamedTuple):
280 input: SampleBlockMeta
281 output: SampleBlockMeta
284def get_input_halo(model: v0_5.ModelDescr, output_halo: PerMember[PerAxis[Halo]]):
285 """returns which halo input tensors need to be divided into blocks with, such that
286 `output_halo` can be cropped from their outputs without introducing gaps."""
287 input_halo: Dict[MemberId, Dict[AxisId, Halo]] = {}
288 outputs = {t.id: t for t in model.outputs}
289 all_tensors = {**{t.id: t for t in model.inputs}, **outputs}
291 for t, th in output_halo.items():
292 axes = {a.id: a for a in outputs[t].axes}
294 for a, ah in th.items():
295 s = axes[a].size
296 if not isinstance(s, v0_5.SizeReference):
297 raise ValueError(
298 f"Unable to map output halo for {t}.{a} to an input axis"
299 )
301 axis = axes[a]
302 ref_axis = {a.id: a for a in all_tensors[s.tensor_id].axes}[s.axis_id]
304 total_output_halo = sum(ah)
305 total_input_halo = total_output_halo * axis.scale / ref_axis.scale
306 assert (
307 total_input_halo == int(total_input_halo) and total_input_halo % 2 == 0
308 )
309 input_halo.setdefault(s.tensor_id, {})[a] = Halo(
310 int(total_input_halo // 2), int(total_input_halo // 2)
311 )
313 return input_halo
316def get_block_transform(
317 model: v0_5.ModelDescr,
318) -> PerMember[PerAxis[Union[LinearSampleAxisTransform, int]]]:
319 """returns how a model's output tensor shapes relates to its input shapes"""
320 ret: Dict[MemberId, Dict[AxisId, Union[LinearSampleAxisTransform, int]]] = {}
321 batch_axis_trf = None
322 for ipt in model.inputs:
323 for a in ipt.axes:
324 if a.type == "batch":
325 batch_axis_trf = LinearSampleAxisTransform(
326 axis=a.id, scale=1, offset=0, member=ipt.id
327 )
328 break
329 if batch_axis_trf is not None:
330 break
331 axis_scales = {
332 t.id: {a.id: a.scale for a in t.axes}
333 for t in chain(model.inputs, model.outputs)
334 }
335 for out in model.outputs:
336 new_axes: Dict[AxisId, Union[LinearSampleAxisTransform, int]] = {}
337 for a in out.axes:
338 if a.size is None:
339 assert a.type == "batch"
340 if batch_axis_trf is None:
341 raise ValueError(
342 "no batch axis found in any input tensor, but output tensor"
343 + f" '{out.id}' has one."
344 )
345 s = batch_axis_trf
346 elif isinstance(a.size, int):
347 s = a.size
348 elif isinstance(a.size, v0_5.DataDependentSize):
349 s = -1
350 elif isinstance(a.size, v0_5.SizeReference):
351 s = LinearSampleAxisTransform(
352 axis=a.size.axis_id,
353 scale=axis_scales[a.size.tensor_id][a.size.axis_id] / a.scale,
354 offset=a.size.offset,
355 member=a.size.tensor_id,
356 )
357 else:
358 assert_never(a.size)
360 new_axes[a.id] = s
362 ret[out.id] = new_axes
364 return ret
367def get_io_sample_block_metas(
368 model: v0_5.ModelDescr,
369 input_sample_shape: PerMember[PerAxis[int]],
370 ns: Mapping[Tuple[MemberId, AxisId], ParameterizedSize_N],
371 batch_size: int = 1,
372) -> Tuple[TotalNumberOfBlocks, Iterable[IO_SampleBlockMeta]]:
373 """returns an iterable yielding meta data for corresponding input and output samples"""
374 if not isinstance(model, v0_5.ModelDescr):
375 raise TypeError(f"get_block_meta() not implemented for {type(model)}")
377 block_axis_sizes = model.get_axis_sizes(ns=ns, batch_size=batch_size)
378 input_block_shape = {
379 t: {aa: s for (tt, aa), s in block_axis_sizes.inputs.items() if tt == t}
380 for t in {tt for tt, _ in block_axis_sizes.inputs}
381 }
382 output_halo = {
383 t.id: {
384 a.id: Halo(a.halo, a.halo) for a in t.axes if isinstance(a, v0_5.WithHalo)
385 }
386 for t in model.outputs
387 }
388 input_halo = get_input_halo(model, output_halo)
390 n_input_blocks, input_blocks = split_multiple_shapes_into_blocks(
391 input_sample_shape, input_block_shape, halo=input_halo
392 )
393 block_transform = get_block_transform(model)
394 return n_input_blocks, (
395 IO_SampleBlockMeta(ipt, ipt.get_transformed(block_transform))
396 for ipt in sample_block_meta_generator(
397 input_blocks, sample_shape=input_sample_shape, sample_id=None
398 )
399 )
402def get_tensor(
403 src: Union[ZipPath, TensorSource],
404 ipt: Union[v0_4.InputTensorDescr, v0_5.InputTensorDescr],
405):
406 """helper to cast/load various tensor sources"""
408 if isinstance(src, Tensor):
409 return src
410 elif isinstance(src, xr.DataArray):
411 return Tensor.from_xarray(src)
412 elif isinstance(src, np.ndarray):
413 return Tensor.from_numpy(src, dims=get_axes_infos(ipt))
414 else:
415 return load_tensor(src, axes=get_axes_infos(ipt))
418def create_sample_for_model(
419 model: AnyModelDescr,
420 *,
421 stat: Optional[Stat] = None,
422 sample_id: SampleId = None,
423 inputs: Union[PerMember[TensorSource], TensorSource],
424) -> Sample:
425 """Create a sample from a single set of input(s) for a specific bioimage.io model
427 Args:
428 model: a bioimage.io model description
429 stat: dictionary with sample and dataset statistics (may be updated in-place!)
430 inputs: the input(s) constituting a single sample.
431 """
433 model_inputs = {get_member_id(d): d for d in model.inputs}
434 if isinstance(inputs, collections.abc.Mapping):
435 inputs = {MemberId(k): v for k, v in inputs.items()}
436 elif len(model_inputs) == 1:
437 inputs = {list(model_inputs)[0]: inputs}
438 else:
439 raise TypeError(
440 f"Expected `inputs` to be a mapping with keys {tuple(model_inputs)}"
441 )
443 if unknown := {k for k in inputs if k not in model_inputs}:
444 raise ValueError(f"Got unexpected inputs: {unknown}")
446 if missing := {
447 k
448 for k, v in model_inputs.items()
449 if k not in inputs and not (isinstance(v, v0_5.InputTensorDescr) and v.optional)
450 }:
451 raise ValueError(f"Missing non-optional model inputs: {missing}")
453 return Sample(
454 members={
455 m: get_tensor(inputs[m], ipt)
456 for m, ipt in model_inputs.items()
457 if m in inputs
458 },
459 stat={} if stat is None else stat,
460 id=sample_id,
461 )
464def load_sample_for_model(
465 *,
466 model: AnyModelDescr,
467 paths: PerMember[Path],
468 axes: Optional[PerMember[Sequence[AxisLike]]] = None,
469 stat: Optional[Stat] = None,
470 sample_id: Optional[SampleId] = None,
471):
472 """load a single sample from `paths` that can be processed by `model`"""
474 if axes is None:
475 axes = {}
477 # make sure members are keyed by MemberId, not string
478 paths = {MemberId(k): v for k, v in paths.items()}
479 axes = {MemberId(k): v for k, v in axes.items()}
481 model_inputs = {get_member_id(d): d for d in model.inputs}
483 if unknown := {k for k in paths if k not in model_inputs}:
484 raise ValueError(f"Got unexpected paths for {unknown}")
486 if unknown := {k for k in axes if k not in model_inputs}:
487 raise ValueError(f"Got unexpected axes hints for: {unknown}")
489 members: Dict[MemberId, Tensor] = {}
490 for m, p in paths.items():
491 if m not in axes:
492 axes[m] = get_axes_infos(model_inputs[m])
493 logger.debug(
494 "loading '{}' from {} with default input axes {} ",
495 m,
496 p,
497 axes[m],
498 )
499 members[m] = load_tensor(p, axes[m])
501 return Sample(
502 members=members,
503 stat={} if stat is None else stat,
504 id=sample_id or tuple(sorted(paths.values())),
505 )