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* Normalize image - cast input images to float32. This is done if the input image isn't of floating type. Issues can occur when do_rescale=False is set in an image processor. When this happens, the image passed to the call is of type uint8 becuase of the type casting that happens in resize because of the PIL image library. As the mean and std values are cast to match the image dtype, this can cause NaNs and infs to appear in the normalized image, as the floating values being used to divide the image are now set to 0. The reason the mean and std values are cast is because previously they were set as float32 by default. However, if the input image was of type float16, the normalization would result in the image being upcast to float32 too. * Add tests * Remove float32 cast |
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| .. | ||
| benchmark | ||
| bettertransformer | ||
| deepspeed | ||
| extended | ||
| fixtures | ||
| fsdp | ||
| generation | ||
| models | ||
| optimization | ||
| peft_integration | ||
| pipelines | ||
| quantization | ||
| repo_utils | ||
| sagemaker | ||
| tokenization | ||
| tools | ||
| trainer | ||
| utils | ||
| __init__.py | ||
| test_backbone_common.py | ||
| test_configuration_common.py | ||
| test_configuration_utils.py | ||
| test_feature_extraction_common.py | ||
| test_feature_extraction_utils.py | ||
| test_image_processing_common.py | ||
| test_image_processing_utils.py | ||
| test_image_transforms.py | ||
| test_modeling_common.py | ||
| test_modeling_flax_common.py | ||
| test_modeling_flax_utils.py | ||
| test_modeling_tf_common.py | ||
| test_modeling_tf_utils.py | ||
| test_modeling_utils.py | ||
| test_pipeline_mixin.py | ||
| test_sequence_feature_extraction_common.py | ||
| test_tokenization_common.py | ||
| test_tokenization_utils.py | ||