Applications: DynaCLR refactor#370
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edyoshikun wants to merge 212 commits intomodular-viscy-stagingfrom
Draft
Applications: DynaCLR refactor#370edyoshikun wants to merge 212 commits intomodular-viscy-stagingfrom
edyoshikun wants to merge 212 commits intomodular-viscy-stagingfrom
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- Add pyproject.toml with hatchling build, torch/timm/monai/numpy deps - Create src layout with _components, unet, contrastive, vae subpackages - Add PEP 561 py.typed marker - Add test scaffolding with device fixture Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add viscy-models to root dependencies and uv sources - Update lockfile with timm and viscy-models dependencies - Verified: uv sync, import, and pytest collection all succeed Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add 06-01-SUMMARY.md with execution results - Update STATE.md with position, metrics, and decisions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- stems.py: UNeXt2Stem, StemDepthtoChannels from v0.3.3 unext2.py - heads.py: PixelToVoxelHead, UnsqueezeHead, PixelToVoxelShuffleHead - blocks.py: icnr_init, _get_convnext_stage, UNeXt2UpStage, UNeXt2Decoder - __init__.py: re-exports all 8 public components - Zero imports from unet/, vae/, or contrastive/ - All attribute names preserved for state dict compatibility
- Copy ConvBlock2D from v0.3.3 source to snake_case file - Copy ConvBlock3D from v0.3.3 source to snake_case file - Preserve register_modules/add_module pattern for state dict key compatibility - Update _layers/__init__.py with public re-exports - Fix docstring formatting for ruff D-series compliance
- 6 tests for ConvBlock2D: forward pass, state dict keys, residual, filter steps, instance norm - 4 tests for ConvBlock3D: forward pass, state dict keys, dropout registration, layer order - All 10 tests verify shape, naming patterns, and module registration
- test_stems.py: UNeXt2Stem shape, StemDepthtoChannels shape + mismatch error - test_heads.py: PixelToVoxelHead, UnsqueezeHead, PixelToVoxelShuffleHead shapes - test_blocks.py: icnr_init, _get_convnext_stage, UNeXt2UpStage, UNeXt2Decoder - 10 tests total, all passing on CPU
- SUMMARY.md with migration details and self-check - STATE.md updated: phase 6 plan 3/3, decisions recorded
- SUMMARY.md documents 8 extracted components with 10 tests - STATE.md updated with decisions from 06-02 execution
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Copy UNeXt2 class (~70 lines) from monolithic unext2.py - Update imports to use viscy_models._components (stems, heads, blocks) - Preserve all attribute names for state dict compatibility - Export UNeXt2 from viscy_models.unet public API Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add tests: default, small backbone, multichannel, diff stack depths, deconv, stem validation - Fix deconv decoder tuple bug in UNeXt2UpStage (trailing comma created tuple not module) - Mark deconv test xfail: original code has channel mismatch in deconv forward path - All 26 tests pass (25 passed, 1 xfailed) with no regressions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add 07-01-SUMMARY.md with execution results and deviation documentation - Update STATE.md: phase 7, plan 1/2 complete, new decisions logged Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Copy FCMAE and all helper classes/functions to unet/fcmae.py - Replace old viscy imports with viscy_models._components imports - Remove duplicated PixelToVoxelShuffleHead (import from _components.heads) - Fix mutable list defaults to tuples (encoder_blocks, dims) - Export both UNeXt2 and FullyConvolutionalMAE from unet/__init__.py
- Copy all 11 test functions with zero logic changes - Update imports from viscy.unet.networks.fcmae to viscy_models.unet.fcmae - Import PixelToVoxelShuffleHead from viscy_models._components.heads - All 37 tests pass across full suite (no regressions)
- Add 07-02-SUMMARY.md with execution results - Update STATE.md: Phase 7 complete, 12 plans total, decisions logged
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add BetaVae25D with VaeUpStage, VaeEncoder, VaeDecoder helpers - Add BetaVaeMonai wrapping MONAI VarAutoEncoder - Fix VaeDecoder mutable list defaults to tuples (COMPAT-02) - Change VaeEncoder pretrained default to False - Preserve all attribute names for state dict compatibility Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…models - Add ContrastiveEncoder with convnext/resnet50 backbone support via timm - Add ResNet3dEncoder with MONAI ResNetFeatures backend - Fix ResNet50 bug: use encoder.num_features instead of encoder.head.fc.in_features - Add pretrained parameter (default False) for pure nn.Module semantics - Preserve state dict attribute names (stem, encoder, projection) - Share projection_mlp utility between both encoder classes
- 3 tests for ContrastiveEncoder: convnext_tiny, resnet50, custom stem - 2 tests for ResNet3dEncoder: resnet18, resnet10 - Verify embedding and projection output shapes - ResNet50 test uses in_stack_depth=10 for valid stem channel alignment
… things to airtable
- Remove api_key/base_id params from AirtableDatasets.__init__; read credentials exclusively from env vars with clear ValueError on missing - Add 59 tests for airtable_utils (database + schemas) with full mocking - Wrap open_ome_zarr in context manager in qc/annotation.py to prevent file handle leaks on exceptions Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…ier, and fix the overwritting by the linear classifiers.
…ension and linear classifier
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