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DiNTS bundles: make search code loadable with torch.load(weights_only=True) - #791
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…ly=True) The search_code_18590.pt files hold numpy arrays (raw TopologySearch.decode() output), which the weights_only=True default of torch.load (PyTorch >= 2.6) refuses. See Project-MONAI/MONAI#9025. - scripts/search.py: save decode() results as tensors - inference.yaml / train.yaml: wrap arch_code in numpy.asarray() and node_a in torch.as_tensor() so both the legacy (numpy) and the new (tensor) files work, with or without the companion MONAI fix (Project-MONAI/MONAI#9143) - inference.yaml / train.yaml: load the search code with map_location='cpu'. It is architecture metadata consumed on the host (TopologyConstruction moves it to its own device; node_a is only indexed in DiNTS.forward), and with a tensor file map_location='cuda' would hand numpy.asarray a CUDA tensor - bump versions / changelog: pancreas 0.5.4, multi_organ 0.0.7 Validated on CPU only (no CUDA machine available). Signed-off-by: 12yuuuu <yu1inge2@gmail.com>
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WalkthroughThe DiNTS training and inference configurations load architecture checkpoints on CPU and convert architecture values for configuration. The pancreas search script saves decoded architecture values as tensors. Both models’ metadata and large-file manifests are updated. ChangesDiNTS checkpoint handling
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix Merge Risk: ⚪ Minimal · up to
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Check name Status Explanation Title check The title clearly and concisely identifies the main change: making DiNTS bundle search code loadable with torch.load(weights_only=True). Description check The description is detailed and relevant. It explains the problem, affected files, implementation, testing, limitations, and required maintainer follow-up. The Status section is missing, and the check… Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request.
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Inline comments:
Review comments at @models/multi_organ_segmentation/configs/inference.yaml:
- Line 9: Convert the checkpoints used by all four `arch_ckpt` loaders to tensor
format and update their corresponding hashes in `large_files.yml`:
`models/multi_organ_segmentation/configs/inference.yaml` line 9,
`models/multi_organ_segmentation/configs/train.yaml` line 12,
`models/pancreas_ct_dints_segmentation/configs/inference.yaml` line 9, and
`models/pancreas_ct_dints_segmentation/configs/train.yaml` line 12. Ensure each
published checkpoint artifact and its recorded hash match.
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models/multi_organ_segmentation/configs/inference.yamlmodels/multi_organ_segmentation/configs/metadata.jsonmodels/multi_organ_segmentation/configs/train.yamlmodels/pancreas_ct_dints_segmentation/configs/inference.yamlmodels/pancreas_ct_dints_segmentation/configs/metadata.jsonmodels/pancreas_ct_dints_segmentation/configs/train.yamlmodels/pancreas_ct_dints_segmentation/scripts/search.py
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Hi @12yuuuu since these files are only about 4kB could you please re-encode them as PyTorch contents and upload them directly into a |
Per review: search_code_18590.pt is only ~4 kB per bundle, so commit it under models/<bundle>/models/ re-encoded as a tensor checkpoint (values and dtypes unchanged, verified against the hosted originals with torch.load(weights_only=True) and numpy.array_equal) and drop the entries from large_files.yml. Update the bundle changelogs accordingly. Signed-off-by: 12yuuuu <yu1inge2@gmail.com>
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Hi @ericspod, done:
The other config changes are kept because released MONAI still uses |
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Hi @12yuuuu I'm going to merge the PR you opened on the MONAI Core side first, this will sort the problem for the current version but not for the old version these bundles rely on. We would have to wait until a release is made with these changes and then update these bundles to use that version, in the meantime your fixes here with the resaved file should work for these older versions. I made suggested changes everywhere to avoid hard-coding the device to use, this should work for those with or without CUDA acceleration and let users override the value of device if they want as well. Other than these changes it looks good to go.
Apply review suggestions: load the search code with map_location=@device and pass @device to the DiNTS search space. Since the arch codes can now be on a GPU, convert them with .cpu().numpy() instead of numpy.asarray(), so older MONAI versions that call torch.from_numpy still get NumPy arrays. Drop the numpy imports that are no longer needed. Signed-off-by: 12yuuuu <yu1inge2@gmail.com>
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Hi @ericspod, thanks. I applied your suggestions ( With this change the arch codes can be loaded onto the GPU, and |
Related: Project-MONAI/MONAI#9025 (issue), Project-MONAI/MONAI#9143 (companion MONAI fix).
Description
models/search_code_18590.ptinpancreas_ct_dints_segmentationandmulti_organ_segmentationstores four
numpy.ndarrayentries (node_a,arch_code_a,arch_code_c,arch_code_a_max) —the raw output of
TopologySearch.decode()saved byscripts/search.py. Since PyTorch 2.6torch.loaddefaults toweights_only=True, whose unpickler rejectsnumpy.core.multiarray._reconstruct, so the bundles fail to load(details and a no-exec inspection of the real file are in the issue thread).
scripts/search.py: convert the decoded codes withtorch.as_tensorbeforetorch.save,so newly produced search codes load under the safe default.
configs/inference.yaml/train.yaml:arch_codeentries wrapped innumpy.asarray(...)andnode_aintorch.as_tensor(...), so the configs work with the legacy numpy file, with atensor-format file, and with MONAI versions before/after MONAI#9143.
configs/inference.yaml/train.yaml:arch_ckptis now loaded withmap_location='cpu'instead of
torch.device('cuda'). The search code is a few hundred bytes of architecturemetadata that is consumed on the host:
TopologyConstructionmovesarch_codeto its owndevice, andnode_ais only indexed in Python insideDiNTS.forward(torch.from_numpyalways produced a CPU tensor there, so this keeps the previous behaviour). With a tensor-format
file,
map_location='cuda'would handnumpy.asarraya CUDA tensor (TypeError: can't convert cuda:0 device type tensor to numpy) and would also refuse to load on machines without CUDA.configs/metadata.json: version + changelog bumped (0.5.3 -> 0.5.4, 0.0.6 -> 0.0.7).Testing
modified
inference.yamlbuilds the network from both the legacy numpy file and a tensor-formatfile (
ConfigParser, withnum_blocks/num_depthsshrunk to match a small synthetic searchcode);
node_astays on CPU as before.map_location='cpu'change is reasoned from documented PyTorch behaviour, not exercised ona GPU here; a run of bundle inference on a CUDA machine (or the premerge CI) would be appreciated.
pre-commit runon the changed files passes (ruff / black / isort / pretty-format-json / check-yaml).Action needed from maintainers
The currently hosted
search_code_18590.pt(NVIDIA CDN / Hugging FaceMONAI/<bundle>)still contains numpy arrays, so
torch.loadin the configs keeps failing under the defaultuntil the file is re-saved with tensors. A converter that re-saves and verifies the file
(
convert_search_code.py) is attached to the issue thread; once re-uploaded,large_files.yml(
hash_val) should be updated — I don't have upload access to either host.Summary by CodeRabbit