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transformers_code_embeddings_v3

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444
idefics3/image_processing_pil_idefics3.py:Idefics3ImageProcessorPil
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[ "BatchFeature", "CONSTANT", "False", "Got", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "LANCZOS", "MAX_IMAGE_SIZE", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "Padding", "PaddingMode", "PilBackend", "Please", "SizeDict", "True", "...
idefics3/image_processing_idefics3.py:Idefics3ImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_image_splitting", "int", "max_image_size", "return_row_col_info", "total" ]
idefics3/image_processing_idefics3.py:_resize_output_size_rescale_to_max_len
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[ "None", "_resize_output_size_rescale_to_max_len", "aspect_ratio", "def", "elif", "else", "height", "if", "int", "is", "max", "max_len", "min_len", "return", "width" ]
idefics3/image_processing_idefics3.py:_resize_output_size_scale_below_upper_bound
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[ "None", "_resize_output_size_scale_below_upper_bound", "and", "aspect_ratio", "def", "elif", "else", "height", "if", "int", "is", "max", "max_len", "return", "width" ]
idefics3/image_processing_idefics3.py:get_resize_output_image_size
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[ "MAX_IMAGE_SIZE", "Model_resize_output_image_size", "_resize_output_size_rescale_to_max_len", "_resize_output_size_scale_below_upper_bound", "def", "height", "image", "max_len", "resolution_max_side", "return", "shape", "width" ]
idefics3/image_processing_idefics3.py:get_max_height_width
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[ "Model_max_height_width", "append", "def", "for", "image", "image_sizes", "images", "images_list", "in", "max", "max_height", "max_width", "return", "shape", "size" ]
idefics3/image_processing_idefics3.py:get_num_channels
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[ "Model_num_channels", "No", "ValueError", "batch", "def", "for", "found", "if", "images", "images_list", "in", "raise", "return", "shape", "the" ]
idefics3/image_processing_idefics3.py:get_device_from_images
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[ "Model_device_from_images", "def", "device", "for", "if", "images", "images_list", "in", "return" ]
idefics3/image_processing_idefics3.py:make_pixel_mask
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[ "Model_pixel_mask", "def", "device", "dtype", "image", "input_height", "input_width", "int", "int64", "mask", "output_size", "return", "shape", "torch", "zeros" ]
idefics3/image_processing_idefics3.py:Idefics3ImageProcessor
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[ "BatchFeature", "False", "Got", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "LANCZOS", "MAX_IMAGE_SIZE", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "Padding", "Please", "SizeDict", "TorchvisionBackend", "True", "ValueError", "__init__",...
vivit/image_processing_vivit.py:make_batched
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[ "Could", "Model", "Model_batched", "ValueError", "and", "batched", "def", "elif", "f", "from", "if", "is_valid_image", "isinstance", "list", "not", "raise", "return", "tuple", "video", "videos" ]
vivit/image_processing_vivit.py:VivitImageProcessor
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[ "BILINEAR", "BaseImageProcessor", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "If", "Image", "Invalid", "It", "ModelImageProcessor", "Must", "None", "PIL", "PILImageResampling", "Tensor", "True", "ValueError", "__in...
efficientnet/image_processing_pil_efficientnet.py:EfficientNetImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "include_top", "rescale_offset", "total" ]
efficientnet/image_processing_pil_efficientnet.py:EfficientNetImageProcessorPil
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[ "BICUBIC", "BatchFeature", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "PILImageResampling", "PilBackend", "True", "__init__", "_preprocess", "append", "center_crop", "class", "crop_size", "data", "def", "do_c...
efficientnet/image_processing_efficientnet.py:EfficientNetImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "include_top", "rescale_offset", "total" ]
efficientnet/image_processing_efficientnet.py:EfficientNetImageProcessor
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[ "BICUBIC", "BatchFeature", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "TorchvisionBackend", "True", "__init__", "_fuse_mean_std_and_rescale_factor", "_preprocess", "and", "center_crop", ...
uvdoc/image_processing_uvdoc.py:UVDocImageProcessor
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[ "BILINEAR", "BatchFeature", "F", "False", "Model", "PILImageResampling", "TorchvisionBackend", "True", "_preprocess", "align_corners", "append", "bilinear", "class", "copy", "data", "def", "device", "dims", "disable_grouping", "do_normalize", "do_rescale", "do_resize", "d...
poolformer/image_processing_pil_poolformer.py:PoolFormerImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "crop_pct", "float", "r", "total" ]
poolformer/image_processing_pil_poolformer.py:PoolFormerImageProcessorPil
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[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Invalid", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PilBackend", "SizeDict", "True", "ValueError", "__init__", "_preprocess...
poolformer/image_processing_poolformer.py:PoolFormerImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "crop_pct", "float", "r", "total" ]
poolformer/image_processing_poolformer.py:PoolFormerImageProcessor
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[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Invalid", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "ValueError", "__init__", "_prepr...
rf_detr/image_processing_rf_detr.py:RfDetrImageProcessorKwargs
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[ "AnnotationFormat", "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_convert_annotations", "format", "r", "str", "total" ]
rf_detr/image_processing_rf_detr.py:binary_mask_to_rle
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[ "Model_mask_to_rle", "concatenate", "def", "flatten", "if", "is_torch_tensor", "list", "mask", "np", "numpy", "pixels", "return", "runs", "where" ]
rf_detr/image_processing_rf_detr.py:convert_segmentation_to_rle
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rf_detr/image_processing_rf_detr.py:convert_coco_poly_to_mask
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rf_detr/image_processing_rf_detr.py:prepare_coco_detection_annotation
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[ "False", "Model_coco_detection_annotation", "None", "annotations", "append", "area", "as_tensor", "bbox", "boxes", "category_id", "class_labels", "classes", "clip", "convert_coco_poly_to_mask", "def", "device", "dtype", "else", "float32", "for", "if", "image", "image_heig...
rf_detr/image_processing_rf_detr.py:RfDetrImageProcessor
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[ "AnnotationFormat", "Any", "BILINEAR", "BatchFeature", "COCO_DETECTION", "COCO_PANOPTIC", "ChannelDimension", "F", "FIRST", "False", "Got", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Make", "ModelImageProcessor", "ModelImageProcessorKwargs", "NEAREST", "None", "PILImageRes...
bit/image_processing_pil_bit.py:BitImageProcessorPil
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bit/image_processing_bit.py:BitImageProcessor
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glm_image/image_processing_pil_glm_image.py:GlmImageImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_pixels", "merge_size", "min_pixels", "patch_size", "r", "temporal_patch_size", "total" ]
glm_image/image_processing_pil_glm_image.py:smart_resize
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glm_image/image_processing_pil_glm_image.py:GlmImageImageProcessorPil
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glm_image/image_processing_glm_image.py:GlmImageImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_pixels", "merge_size", "min_pixels", "patch_size", "r", "temporal_patch_size", "total" ]
glm_image/image_processing_glm_image.py:smart_resize
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glm_image/image_processing_glm_image.py:GlmImageImageProcessor
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[ "BICUBIC", "BatchFeature", "False", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "_standardize_kwargs", "and", "auto_docstring", "batch_s...
gemma3/image_processing_pil_gemma3.py:Gemma3ImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_pan_and_scan", "float", "int", "pan_and_scan_max_num_crops", "pan_and_scan_min_crop_size", "pan_and_scan_min_ratio_to_activate", "total" ]
gemma3/image_processing_pil_gemma3.py:Gemma3ImageProcessorPil
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[ "BILINEAR", "BatchFeature", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PilBackend", "True", "__init__", "_preprocess", "_process_images_for_pan_and_scan", "append", "auto_docstring", "ceil", ...
gemma3/image_processing_gemma3.py:Gemma3ImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_pan_and_scan", "float", "int", "pan_and_scan_max_num_crops", "pan_and_scan_min_crop_size", "pan_and_scan_min_ratio_to_activate", "total" ]
gemma3/image_processing_gemma3.py:Gemma3ImageProcessor
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[ "BILINEAR", "BatchFeature", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "TorchvisionBackend", "True", "_", "__init__", "_preprocess", "_process_images_for_pan_and_scan", "auto_docstring", "ceil", ...
pix2struct/image_processing_pix2struct.py:Pix2StructImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "bool", "class", "header_text", "int", "is_vqa", "list", "max_patches", "patch_size", "str", "total" ]
pix2struct/image_processing_pix2struct.py:render_text
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[ "Arial", "BytesIO", "DEFAULT_FONT_PATH", "Draw", "Image", "ImageDraw", "ImageFont", "Model_text", "None", "RGB", "TTF", "TextWrapper", "UTF", "_", "and", "background_color", "black", "bottom_padding", "def", "draw", "elif", "else", "encoding", "fill", "font", "font_...
pix2struct/image_processing_pix2struct.py:torch_extract_patches
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[ "Model", "Model_extract_patches", "batch_size", "channels", "def", "functional", "height", "image_tensor", "nn", "patch_height", "patch_width", "patches", "permute", "reshape", "return", "shape", "stride", "unfold", "width" ]
pix2struct/image_processing_pix2struct.py:Pix2StructImageProcessor
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[ "BILINEAR", "BatchFeature", "ChannelDimension", "FIRST", "False", "Image", "InterpolationMode", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "RGB", "SizeDict", "TorchvisionBackend", "True", "_prepare_image_like_inputs", "_preprocess", "_preprocess_image_like_inputs", ...
pix2struct/image_processing_pil_pix2struct.py:Pix2StructImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "bool", "class", "header_text", "int", "is_vqa", "list", "max_patches", "patch_size", "str", "total" ]
pix2struct/image_processing_pil_pix2struct.py:render_text
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pix2struct/image_processing_pil_pix2struct.py:Pix2StructImageProcessorPil
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[ "BatchFeature", "ChannelDimension", "FIRST", "False", "Image", "LAST", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PilBackend", "RGB", "SizeDict", "True", "_prepare_image_like_inputs", "_preprocess", "_preprocess_image_like_inputs", "_standardize_kwargs", "_vali...
convnext/image_processing_convnext.py:ConvNextImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "crop_pct", "float", "r", "total" ]
convnext/image_processing_convnext.py:ConvNextImageProcessor
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[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "center_crop", "class", ...
convnext/image_processing_pil_convnext.py:ConvNextImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "crop_pct", "float", "r", "total" ]
convnext/image_processing_pil_convnext.py:ConvNextImageProcessorPil
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chameleon/image_processing_chameleon.py:ChameleonImageProcessor
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[ "False", "Image", "LANCZOS", "ModelImageProcessor", "PIL", "PILImageResampling", "RGB", "RGBA", "TorchvisionBackend", "True", "__init__", "alpha", "any", "array", "astype", "class", "convert", "convert_to_rgb", "crop_size", "def", "default_to_square", "do_center_crop", "d...
chameleon/image_processing_pil_chameleon.py:ChameleonImageProcessorPil
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vilt/image_processing_vilt.py:ViltImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "r", "size_divisor", "total" ]
vilt/image_processing_vilt.py:ViltImageProcessor
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vilt/image_processing_pil_vilt.py:max_across_indices
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[ "Model", "Model_across_indices", "def", "for", "in", "return", "values", "values_i", "zip" ]
vilt/image_processing_pil_vilt.py:make_pixel_mask
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[ "Model_pixel_mask", "None", "channel_dim", "def", "dtype", "get_image_size", "image", "input_data_format", "input_height", "input_width", "int", "int64", "mask", "np", "output_size", "return", "zeros" ]
vilt/image_processing_pil_vilt.py:get_resize_output_image_size
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vilt/image_processing_pil_vilt.py:ViltImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "r", "size_divisor", "total" ]
vilt/image_processing_pil_vilt.py:ViltImageProcessorPil
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[ "BICUBIC", "BatchFeature", "CONSTANT", "ChannelDimension", "FIRST", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "MAX_LONGER_EDGE", "MAX_SHORTER_EDGE", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PaddingMode", "PilBackend", "Si...
rt_detr/image_processing_rt_detr.py:RTDetrImageProcessorKwargs
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[ "AnnotationFormat", "False", "ImagesKwargs", "Model", "bool", "class", "do_convert_annotations", "format", "r", "str", "total" ]
rt_detr/image_processing_rt_detr.py:prepare_coco_detection_annotation
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rt_detr/image_processing_rt_detr.py:RTDetrImageProcessor
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[ "AnnotationFormat", "Any", "BILINEAR", "BatchFeature", "COCO_DETECTION", "ChannelDimension", "FIRST", "False", "Got", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Make", "Model", "ModelKwargs", "NEAREST", "None", "PILImageResampling", "Padding", "Please", "SUPPORTED_ANNOTA...
rt_detr/image_processing_pil_rt_detr.py:RTDetrImageProcessorKwargs
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[ "AnnotationFormat", "False", "ImagesKwargs", "Model", "bool", "class", "do_convert_annotations", "format", "r", "str", "total" ]
rt_detr/image_processing_pil_rt_detr.py:prepare_coco_detection_annotation_pil
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rt_detr/image_processing_pil_rt_detr.py:RTDetrImageProcessorPil
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prompt_depth_anything/image_processing_pil_prompt_depth_anything.py:_constrain_to_multiple_of
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prompt_depth_anything/image_processing_pil_prompt_depth_anything.py:_get_resize_output_image_size
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prompt_depth_anything/image_processing_pil_prompt_depth_anything.py:PromptDepthAnythingImageProcessorKwargs
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prompt_depth_anything/image_processing_pil_prompt_depth_anything.py:PromptDepthAnythingImageProcessorPil
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prompt_depth_anything/image_processing_prompt_depth_anything.py:PromptDepthAnythingImageProcessorKwargs
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prompt_depth_anything/image_processing_prompt_depth_anything.py:_constrain_to_multiple_of
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prompt_depth_anything/image_processing_prompt_depth_anything.py:_get_resize_output_image_size
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prompt_depth_anything/image_processing_prompt_depth_anything.py:PromptDepthAnythingImageProcessor
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flava/image_processing_flava.py:FlavaImageProcessorKwargs
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flava/image_processing_flava.py:FlavaMaskingGenerator
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flava/image_processing_flava.py:FlavaImageProcessor
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flava/image_processing_pil_flava.py:FlavaImageProcessorKwargs
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flava/image_processing_pil_flava.py:FlavaMaskingGenerator
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flava/image_processing_pil_flava.py:FlavaImageProcessorPil
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minicpmv4_6/image_processing_minicpmv4_6.py:ensure_divide
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minicpmv4_6/image_processing_minicpmv4_6.py:MiniCPMV4_6ImageProcessorKwargs
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minicpmv4_6/image_processing_minicpmv4_6.py:MiniCPMV4_6ImageProcessor
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minicpmv4_6/image_processing_pil_minicpmv4_6.py:ensure_divide
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minicpmv4_6/image_processing_pil_minicpmv4_6.py:MiniCPMV4_6ImageProcessorPilKwargs
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minicpmv4_6/image_processing_pil_minicpmv4_6.py:MiniCPMV4_6ImageProcessorPil
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videomae/image_processing_pil_videomae.py:VideoMAEImageProcessorPil
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videomae/image_processing_videomae.py:VideoMAEImageProcessor
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tipsv2/image_processing_tipsv2.py:Tipsv2ImageProcessor
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gemma4_unified/image_processing_gemma4_unified.py:Gemma4UnifiedImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_soft_tokens", "patch_size", "pooling_kernel_size", "total" ]
gemma4_unified/image_processing_gemma4_unified.py:get_aspect_ratio_preserving_size
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gemma4_unified/image_processing_gemma4_unified.py:convert_image_to_patches
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gemma4_unified/image_processing_gemma4_unified.py:pad_along_first_dim
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gemma4_unified/image_processing_gemma4_unified.py:patches_merge
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gemma4_unified/image_processing_gemma4_unified.py:Gemma4UnifiedImageProcessor
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eomt/image_processing_pil_eomt.py:convert_segmentation_map_to_binary_masks
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eomt/image_processing_pil_eomt.py:check_segment_validity
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eomt/image_processing_pil_eomt.py:EomtImageProcessorKwargs
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[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "bool", "class", "do_split_image", "ignore_index", "int", "r", "total" ]
eomt/image_processing_pil_eomt.py:compute_segments
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eomt/image_processing_pil_eomt.py:get_target_size
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[ "Model_tarModel_size", "def", "int", "longest_edge", "or", "return", "shortest_edge", "size_dict", "tarModel_height", "tarModel_width" ]
eomt/image_processing_pil_eomt.py:remove_low_and_no_objects
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[ "Model_low_and_no_objects", "ValueError", "and", "def", "have", "if", "labels", "mask", "masks", "must", "ne", "not", "num_labels", "object_mask_threshold", "raise", "return", "same", "scores", "shape", "the", "to_keep" ]