Instructions to use hf-internal-testing/tiny-random-GroupViTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GroupViTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-GroupViTModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-GroupViTModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-GroupViTModel") - Notebooks
- Google Colab
- Kaggle
fix batchnorm dimension issue
#4
by prathikr - opened
According to the ONNX Spec for BatchNormalization, there is no explicitly defined behavior for input of rank 2. There is precedent for input of rank 1 where we can assume a 2nd dimension of size 1. Extending that precedent, this change adds an unsqueeze node before all BatchNormalization operators to add a 3rd dimension of size 1 and then squeezes away that dimension for downstream operators.
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