Fix/transformers runtime patch#2
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- LISAT.py: Add transformers version patch, use kwargs.get() with defaults - builder.py: Fallback to openai/clip-vit-large-patch14 when saved path doesn't exist Fixes issues reported in GitHub: missing train_mask_decoder kwargs, hardcoded vision tower paths from original author's machine causing ValueError.
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Summary
Fixes several issues that prevent loading pretrained LISAT models in different environments.
Problems Fixed
1.
ValueError: Unknown vision tower: /home/patrickwu/...The saved model config contains hardcoded absolute paths from the original training machine. When users download the model, this path doesn't exist.
Fix:
builder.pynow falls back toopenai/clip-vit-large-patch14when the config path doesn't exist locally.2.
KeyError: 'train_mask_decoder'During inference loading via
from_pretrained(), kwargs liketrain_mask_decoderandout_dimaren't passed, causing a KeyError.Fix:
LISAT.pyandLISAT_eval.pynow usekwargs.get()with sensible defaults.3. Transformers version/registration errors
Strict version checks in
transformerscauseImportErrorat import time. Additionally, duplicateAutoConfig.registercalls raiseValueError: already used.Fix: Runtime monkey-patch disables strict version checks before importing transformers, and wraps registration methods to ignore duplicates.
Files Changed
model/LISAT.py— kwargs defaults + transformers patchmodel/LISAT_eval.py— kwargs defaults + transformers patchmodel/llava/model/multimodal_encoder/builder.py— vision tower fallbackTesting
py_compileContribution by @wildcraft958