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b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--10000_chkpt/processing_prismatic.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2daaf82c3b8946fe57596f656f14a031eaef93 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--10000_chkpt/processing_prismatic.py @@ -0,0 +1,257 @@ +""" +processing_prismatic.py + +HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration +specifies `siglip-224px+7b`. +""" + +from typing import Any, ClassVar, List, Optional, Tuple, Union + +import timm.data +import torch +import torchvision.transforms.functional as TVF +from PIL import Image +from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor +from transformers import PreTrainedTokenizerBase +from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy +from transformers.utils import TensorType + + +# === Image Processing === +def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image: + """Given a PIL.Image, pad to square by adding a symmetric border around the height/width.""" + (w, h), max_wh = image.size, max(image.size) + horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2) + padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad) + + return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant") + + +class PrismaticImageProcessor(ImageProcessingMixin): + model_input_names: ClassVar[List[str]] = ["pixel_values"] + + def __init__( + self, + use_fused_vision_backbone: bool = False, + image_resize_strategy: str = "letterbox", + input_sizes: Optional[List[Tuple[int, int, int]]] = None, + interpolations: Optional[List[str]] = None, + means: Optional[List[Tuple[float, float, float]]] = None, + stds: Optional[List[Tuple[float, float, float]]] = None, + **kwargs: str, + ) -> None: + """ + Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be + created by TIMM, and edited to follow our custom `image_resize_strategy` logic. + + @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone + @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox > + @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height) + @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic") + @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`) + @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`) + """ + self.use_fused_vision_backbone = use_fused_vision_backbone + self.image_resize_strategy = image_resize_strategy + + # Handle `None` default values + input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes + means = [(0.5, 0.5, 0.5)] if means is None else means + stds = [(0.5, 0.5, 0.5)] if stds is None else stds + + # TIMM `data_cfg` Parameters + self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds + + # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values! + self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], [] + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + for idx in range(len(input_sizes)): + transform = timm.data.create_transform( + input_size=self.input_sizes[idx], + interpolation=self.interpolations[idx], + mean=self.means[idx], + std=self.stds[idx], + crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`) + crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0` + is_training=False, # No image augmentations when loading the transform! + ) + + # [Validation] Ensure appropriate transform structure, expected sizes + if not ( + isinstance(transform, Compose) + and (len(transform.transforms) == 4) + and isinstance(transform.transforms[0], Resize) + and isinstance(transform.transforms[1], CenterCrop) + and isinstance(transform.transforms[2], ToTensor) + and isinstance(transform.transforms[3], Normalize) + and (transform.transforms[0].size == self.input_sizes[idx][-1]) + and (transform.transforms[1].size == self.input_sizes[idx][-2:]) + ): + raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`") + + # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute. + # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`) + resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3] + self.tvf_resize_params.append( + { + "size": resize_t.size, + "interpolation": TVF.pil_modes_mapping[resize_t.interpolation], + "max_size": None, + "antialias": True, + } + ) + self.tvf_crop_params.append({"output_size": crop_t.size}) + self.tvf_normalize_params.append( + { + "mean": norm_t.mean.float().numpy().tolist(), + "std": norm_t.std.float().numpy().tolist(), + "inplace": False, + } + ) + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + # Handle Prismatic `image_resize_strategy` + if self.image_resize_strategy == "resize-naive": + self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size) + elif self.image_resize_strategy == "letterbox": + self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]]) + elif self.image_resize_strategy == "resize-crop": + pass + else: + raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!") + + # Dispatch **kwargs to super() + super().__init__(**kwargs) + + def apply_transform(self, img: Image.Image) -> torch.Tensor: + """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])""" + if self.tvf_do_letterbox: + img = letterbox_pad_transform(img, self.tvf_letterbox_fill) + + # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side! + imgs_t = [] + for idx in range(len(self.input_sizes)): + img_idx = TVF.resize(img, **self.tvf_resize_params[idx]) + img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx]) + img_idx_t = TVF.to_tensor(img_idx) + img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx]) + imgs_t.append(img_idx_t) + + # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0 + img_t = torch.vstack(imgs_t) + + return img_t + + def preprocess( + self, + images: Union[Image.Image, List[Image.Image]], + return_tensors: Optional[Union[str, TensorType]] = None, + **_: str, + ) -> BatchFeature: + """ + Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we + explicitly only handle PIL.Image.Image instances for simplicity. + + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray + + @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values" + """ + if not isinstance(images, list): + images = [images] + + # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor + pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images]) + + # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert + return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors) + + def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature: + return self.preprocess(images, **kwargs) + + +# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer === +# =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py +class PrismaticProcessor(ProcessorMixin): + attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"] + image_processor_class: str = "AutoImageProcessor" + tokenizer_class: str = "AutoTokenizer" + + def __init__( + self, + image_processor: Optional[ImageProcessingMixin] = None, + tokenizer: Optional[PreTrainedTokenizerBase] = None, + ) -> None: + super().__init__(image_processor, tokenizer) + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], + images: Union[Image.Image, List[Image.Image]], + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Optional[Union[bool, str, TruncationStrategy]] = None, + max_length: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + ) -> BatchFeature: + """ + Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer, + forwards images to PrismaticImageProcessor. + + @param text: The (batch) of text to encode; must be a string or list of strings. + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False > + @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified + @param max_length: Maximum length (in tokens) to truncate + @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH) + + @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`. + """ + pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"] + text_inputs = self.tokenizer( + text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length + ) + + # [Validate] Need same number of images and text inputs! + if pixel_values.shape[0] != text_inputs.input_ids.shape[0]: + raise ValueError("Batch is malformed; expected same number of images and text inputs!") + + return BatchFeature(data={**text_inputs, "pixel_values": pixel_values}) + + # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation === + def batch_decode( + self, + sequences: Union[List[int], List[List[int]], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> List[str]: + return self.tokenizer.batch_decode( + sequences=sequences, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + def decode( + self, + token_ids: Union[int, List[int], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> str: + return self.tokenizer.decode( + token_ids=token_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def model_input_names(self) -> List[str]: + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--10000_chkpt/processor_config.json b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--10000_chkpt/processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f892f8a8f53e2f3603a7cf86043a1add317c1787 --- /dev/null +++ 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0000000000000000000000000000000000000000..f1ab3ba06c55334e458eab8a54eede4623ef0112 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--15000_chkpt/lora_adapter/adapter_model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6953184b3d66420b187fb330434548d16a42a202b773d857a177a7cd75cfd9f +size 484467800 diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--15000_chkpt/processing_prismatic.py b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--15000_chkpt/processing_prismatic.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2daaf82c3b8946fe57596f656f14a031eaef93 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--15000_chkpt/processing_prismatic.py @@ -0,0 +1,257 @@ +""" +processing_prismatic.py + +HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration +specifies `siglip-224px+7b`. +""" + +from typing import Any, ClassVar, List, Optional, Tuple, Union + +import timm.data +import torch +import torchvision.transforms.functional as TVF +from PIL import Image +from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor +from transformers import PreTrainedTokenizerBase +from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy +from transformers.utils import TensorType + + +# === Image Processing === +def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image: + """Given a PIL.Image, pad to square by adding a symmetric border around the height/width.""" + (w, h), max_wh = image.size, max(image.size) + horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2) + padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad) + + return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant") + + +class PrismaticImageProcessor(ImageProcessingMixin): + model_input_names: ClassVar[List[str]] = ["pixel_values"] + + def __init__( + self, + use_fused_vision_backbone: bool = False, + image_resize_strategy: str = "letterbox", + input_sizes: Optional[List[Tuple[int, int, int]]] = None, + interpolations: Optional[List[str]] = None, + means: Optional[List[Tuple[float, float, float]]] = None, + stds: Optional[List[Tuple[float, float, float]]] = None, + **kwargs: str, + ) -> None: + """ + Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be + created by TIMM, and edited to follow our custom `image_resize_strategy` logic. + + @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone + @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox > + @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height) + @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic") + @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`) + @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`) + """ + self.use_fused_vision_backbone = use_fused_vision_backbone + self.image_resize_strategy = image_resize_strategy + + # Handle `None` default values + input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes + means = [(0.5, 0.5, 0.5)] if means is None else means + stds = [(0.5, 0.5, 0.5)] if stds is None else stds + + # TIMM `data_cfg` Parameters + self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds + + # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values! + self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], [] + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + for idx in range(len(input_sizes)): + transform = timm.data.create_transform( + input_size=self.input_sizes[idx], + interpolation=self.interpolations[idx], + mean=self.means[idx], + std=self.stds[idx], + crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`) + crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0` + is_training=False, # No image augmentations when loading the transform! + ) + + # [Validation] Ensure appropriate transform structure, expected sizes + if not ( + isinstance(transform, Compose) + and (len(transform.transforms) == 4) + and isinstance(transform.transforms[0], Resize) + and isinstance(transform.transforms[1], CenterCrop) + and isinstance(transform.transforms[2], ToTensor) + and isinstance(transform.transforms[3], Normalize) + and (transform.transforms[0].size == self.input_sizes[idx][-1]) + and (transform.transforms[1].size == self.input_sizes[idx][-2:]) + ): + raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`") + + # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute. + # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`) + resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3] + self.tvf_resize_params.append( + { + "size": resize_t.size, + "interpolation": TVF.pil_modes_mapping[resize_t.interpolation], + "max_size": None, + "antialias": True, + } + ) + self.tvf_crop_params.append({"output_size": crop_t.size}) + self.tvf_normalize_params.append( + { + "mean": norm_t.mean.float().numpy().tolist(), + "std": norm_t.std.float().numpy().tolist(), + "inplace": False, + } + ) + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + # Handle Prismatic `image_resize_strategy` + if self.image_resize_strategy == "resize-naive": + self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size) + elif self.image_resize_strategy == "letterbox": + self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]]) + elif self.image_resize_strategy == "resize-crop": + pass + else: + raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!") + + # Dispatch **kwargs to super() + super().__init__(**kwargs) + + def apply_transform(self, img: Image.Image) -> torch.Tensor: + """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])""" + if self.tvf_do_letterbox: + img = letterbox_pad_transform(img, self.tvf_letterbox_fill) + + # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side! + imgs_t = [] + for idx in range(len(self.input_sizes)): + img_idx = TVF.resize(img, **self.tvf_resize_params[idx]) + img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx]) + img_idx_t = TVF.to_tensor(img_idx) + img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx]) + imgs_t.append(img_idx_t) + + # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0 + img_t = torch.vstack(imgs_t) + + return img_t + + def preprocess( + self, + images: Union[Image.Image, List[Image.Image]], + return_tensors: Optional[Union[str, TensorType]] = None, + **_: str, + ) -> BatchFeature: + """ + Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we + explicitly only handle PIL.Image.Image instances for simplicity. + + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray + + @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values" + """ + if not isinstance(images, list): + images = [images] + + # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor + pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images]) + + # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert + return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors) + + def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature: + return self.preprocess(images, **kwargs) + + +# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer === +# =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py +class PrismaticProcessor(ProcessorMixin): + attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"] + image_processor_class: str = "AutoImageProcessor" + tokenizer_class: str = "AutoTokenizer" + + def __init__( + self, + image_processor: Optional[ImageProcessingMixin] = None, + tokenizer: Optional[PreTrainedTokenizerBase] = None, + ) -> None: + super().__init__(image_processor, tokenizer) + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], + images: Union[Image.Image, List[Image.Image]], + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Optional[Union[bool, str, TruncationStrategy]] = None, + max_length: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + ) -> BatchFeature: + """ + Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer, + forwards images to PrismaticImageProcessor. + + @param text: The (batch) of text to encode; must be a string or list of strings. + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False > + @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified + @param max_length: Maximum length (in tokens) to truncate + @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH) + + @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`. + """ + pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"] + text_inputs = self.tokenizer( + text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length + ) + + # [Validate] Need same number of images and text inputs! + if pixel_values.shape[0] != text_inputs.input_ids.shape[0]: + raise ValueError("Batch is malformed; expected same number of images and text inputs!") + + return BatchFeature(data={**text_inputs, "pixel_values": pixel_values}) + + # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation === + def batch_decode( + self, + sequences: Union[List[int], List[List[int]], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> List[str]: + return self.tokenizer.batch_decode( + sequences=sequences, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + def decode( + self, + token_ids: Union[int, List[int], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> str: + return self.tokenizer.decode( + token_ids=token_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def model_input_names(self) -> List[str]: + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--15000_chkpt/proprio_projector--15000_checkpoint.pt b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--15000_chkpt/proprio_projector--15000_checkpoint.pt new file mode 100644 index 0000000000000000000000000000000000000000..6374998a8eac663794f340ffc5c023146855fe1e --- /dev/null +++ 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b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--25000_chkpt/processing_prismatic.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2daaf82c3b8946fe57596f656f14a031eaef93 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--25000_chkpt/processing_prismatic.py @@ -0,0 +1,257 @@ +""" +processing_prismatic.py + +HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration +specifies `siglip-224px+7b`. +""" + +from typing import Any, ClassVar, List, Optional, Tuple, Union + +import timm.data +import torch +import torchvision.transforms.functional as TVF +from PIL import Image +from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor +from transformers import PreTrainedTokenizerBase +from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy +from transformers.utils import TensorType + + +# === Image Processing === +def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image: + """Given a PIL.Image, pad to square by adding a symmetric border around the height/width.""" + (w, h), max_wh = image.size, max(image.size) + horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2) + padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad) + + return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant") + + +class PrismaticImageProcessor(ImageProcessingMixin): + model_input_names: ClassVar[List[str]] = ["pixel_values"] + + def __init__( + self, + use_fused_vision_backbone: bool = False, + image_resize_strategy: str = "letterbox", + input_sizes: Optional[List[Tuple[int, int, int]]] = None, + interpolations: Optional[List[str]] = None, + means: Optional[List[Tuple[float, float, float]]] = None, + stds: Optional[List[Tuple[float, float, float]]] = None, + **kwargs: str, + ) -> None: + """ + Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be + created by TIMM, and edited to follow our custom `image_resize_strategy` logic. + + @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone + @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox > + @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height) + @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic") + @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`) + @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`) + """ + self.use_fused_vision_backbone = use_fused_vision_backbone + self.image_resize_strategy = image_resize_strategy + + # Handle `None` default values + input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes + means = [(0.5, 0.5, 0.5)] if means is None else means + stds = [(0.5, 0.5, 0.5)] if stds is None else stds + + # TIMM `data_cfg` Parameters + self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds + + # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values! + self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], [] + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + for idx in range(len(input_sizes)): + transform = timm.data.create_transform( + input_size=self.input_sizes[idx], + interpolation=self.interpolations[idx], + mean=self.means[idx], + std=self.stds[idx], + crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`) + crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0` + is_training=False, # No image augmentations when loading the transform! + ) + + # [Validation] Ensure appropriate transform structure, expected sizes + if not ( + isinstance(transform, Compose) + and (len(transform.transforms) == 4) + and isinstance(transform.transforms[0], Resize) + and isinstance(transform.transforms[1], CenterCrop) + and isinstance(transform.transforms[2], ToTensor) + and isinstance(transform.transforms[3], Normalize) + and (transform.transforms[0].size == self.input_sizes[idx][-1]) + and (transform.transforms[1].size == self.input_sizes[idx][-2:]) + ): + raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`") + + # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute. + # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`) + resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3] + self.tvf_resize_params.append( + { + "size": resize_t.size, + "interpolation": TVF.pil_modes_mapping[resize_t.interpolation], + "max_size": None, + "antialias": True, + } + ) + self.tvf_crop_params.append({"output_size": crop_t.size}) + self.tvf_normalize_params.append( + { + "mean": norm_t.mean.float().numpy().tolist(), + "std": norm_t.std.float().numpy().tolist(), + "inplace": False, + } + ) + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + # Handle Prismatic `image_resize_strategy` + if self.image_resize_strategy == "resize-naive": + self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size) + elif self.image_resize_strategy == "letterbox": + self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]]) + elif self.image_resize_strategy == "resize-crop": + pass + else: + raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!") + + # Dispatch **kwargs to super() + super().__init__(**kwargs) + + def apply_transform(self, img: Image.Image) -> torch.Tensor: + """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])""" + if self.tvf_do_letterbox: + img = letterbox_pad_transform(img, self.tvf_letterbox_fill) + + # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side! + imgs_t = [] + for idx in range(len(self.input_sizes)): + img_idx = TVF.resize(img, **self.tvf_resize_params[idx]) + img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx]) + img_idx_t = TVF.to_tensor(img_idx) + img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx]) + imgs_t.append(img_idx_t) + + # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0 + img_t = torch.vstack(imgs_t) + + return img_t + + def preprocess( + self, + images: Union[Image.Image, List[Image.Image]], + return_tensors: Optional[Union[str, TensorType]] = None, + **_: str, + ) -> BatchFeature: + """ + Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we + explicitly only handle PIL.Image.Image instances for simplicity. + + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray + + @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values" + """ + if not isinstance(images, list): + images = [images] + + # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor + pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images]) + + # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert + return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors) + + def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature: + return self.preprocess(images, **kwargs) + + +# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer === +# =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py +class PrismaticProcessor(ProcessorMixin): + attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"] + image_processor_class: str = "AutoImageProcessor" + tokenizer_class: str = "AutoTokenizer" + + def __init__( + self, + image_processor: Optional[ImageProcessingMixin] = None, + tokenizer: Optional[PreTrainedTokenizerBase] = None, + ) -> None: + super().__init__(image_processor, tokenizer) + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], + images: Union[Image.Image, List[Image.Image]], + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Optional[Union[bool, str, TruncationStrategy]] = None, + max_length: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + ) -> BatchFeature: + """ + Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer, + forwards images to PrismaticImageProcessor. + + @param text: The (batch) of text to encode; must be a string or list of strings. + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False > + @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified + @param max_length: Maximum length (in tokens) to truncate + @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH) + + @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`. + """ + pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"] + text_inputs = self.tokenizer( + text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length + ) + + # [Validate] Need same number of images and text inputs! + if pixel_values.shape[0] != text_inputs.input_ids.shape[0]: + raise ValueError("Batch is malformed; expected same number of images and text inputs!") + + return BatchFeature(data={**text_inputs, "pixel_values": pixel_values}) + + # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation === + def batch_decode( + self, + sequences: Union[List[int], List[List[int]], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> List[str]: + return self.tokenizer.batch_decode( + sequences=sequences, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + def decode( + self, + token_ids: Union[int, List[int], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> str: + return self.tokenizer.decode( + token_ids=token_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def model_input_names(self) -> List[str]: + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--25000_chkpt/processor_config.json b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--25000_chkpt/processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f892f8a8f53e2f3603a7cf86043a1add317c1787 --- /dev/null +++ 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b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--35000_chkpt/lora_adapter/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4b77e73971fcfecd5b4e3ce849a6b364302bc8d0 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--35000_chkpt/lora_adapter/README.md @@ -0,0 +1,202 @@ +--- +base_model: /inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/ai_models/openvla/openvla-7b +library_name: peft +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.11.1 \ No newline at end of file diff --git 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b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/lora_adapter/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4b77e73971fcfecd5b4e3ce849a6b364302bc8d0 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/lora_adapter/README.md @@ -0,0 +1,202 @@ +--- +base_model: /inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/ai_models/openvla/openvla-7b +library_name: peft +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.11.1 \ No newline at end of file diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/lora_adapter/adapter_model.safetensors b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/lora_adapter/adapter_model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..4e7becd293b797092bf61da55d60f782073d6701 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/lora_adapter/adapter_model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f037b54d36e0369e95e410dd9105c6601f1be6f1b828b108594e63d59c75401 +size 484467800 diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/preprocessor_config.json b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/preprocessor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..92975ace548590d24f65f497d7610af3b92b4dcd --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/preprocessor_config.json @@ -0,0 +1,114 @@ +{ + "auto_map": { + "AutoImageProcessor": "processing_prismatic.PrismaticImageProcessor", + "AutoProcessor": "processing_prismatic.PrismaticProcessor" + }, + "image_processor_type": "PrismaticImageProcessor", + "image_resize_strategy": "resize-naive", + "input_sizes": [ + [ + 3, + 224, + 224 + ], + [ + 3, + 224, + 224 + ] + ], + "interpolations": [ + "bicubic", + "bicubic" + ], + "means": [ + [ + 0.485, + 0.456, + 0.406 + ], + [ + 0.5, + 0.5, + 0.5 + ] + ], + "processor_class": "PrismaticProcessor", + "stds": [ + [ + 0.229, + 0.224, + 0.225 + ], + [ + 0.5, + 0.5, + 0.5 + ] + ], + "tvf_crop_params": [ + { + "output_size": [ + 224, + 224 + ] + }, + { + "output_size": [ + 224, + 224 + ] + } + ], + "tvf_do_letterbox": false, + "tvf_letterbox_fill": null, + "tvf_normalize_params": [ + { + "inplace": false, + "mean": [ + 0.484375, + 0.455078125, + 0.40625 + ], + "std": [ + 0.228515625, + 0.2236328125, + 0.224609375 + ] + }, + { + "inplace": false, + "mean": [ + 0.5, + 0.5, + 0.5 + ], + "std": [ + 0.5, + 0.5, + 0.5 + ] + } + ], + "tvf_resize_params": [ + { + "antialias": true, + "interpolation": 3, + "max_size": null, + "size": [ + 224, + 224 + ] + }, + { + "antialias": true, + "interpolation": 3, + "max_size": null, + "size": [ + 224, + 224 + ] + } + ], + "use_fused_vision_backbone": true +} diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/processing_prismatic.py b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/processing_prismatic.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2daaf82c3b8946fe57596f656f14a031eaef93 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/processing_prismatic.py @@ -0,0 +1,257 @@ +""" +processing_prismatic.py + +HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration +specifies `siglip-224px+7b`. +""" + +from typing import Any, ClassVar, List, Optional, Tuple, Union + +import timm.data +import torch +import torchvision.transforms.functional as TVF +from PIL import Image +from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor +from transformers import PreTrainedTokenizerBase +from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy +from transformers.utils import TensorType + + +# === Image Processing === +def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image: + """Given a PIL.Image, pad to square by adding a symmetric border around the height/width.""" + (w, h), max_wh = image.size, max(image.size) + horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2) + padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad) + + return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant") + + +class PrismaticImageProcessor(ImageProcessingMixin): + model_input_names: ClassVar[List[str]] = ["pixel_values"] + + def __init__( + self, + use_fused_vision_backbone: bool = False, + image_resize_strategy: str = "letterbox", + input_sizes: Optional[List[Tuple[int, int, int]]] = None, + interpolations: Optional[List[str]] = None, + means: Optional[List[Tuple[float, float, float]]] = None, + stds: Optional[List[Tuple[float, float, float]]] = None, + **kwargs: str, + ) -> None: + """ + Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be + created by TIMM, and edited to follow our custom `image_resize_strategy` logic. + + @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone + @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox > + @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height) + @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic") + @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`) + @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`) + """ + self.use_fused_vision_backbone = use_fused_vision_backbone + self.image_resize_strategy = image_resize_strategy + + # Handle `None` default values + input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes + means = [(0.5, 0.5, 0.5)] if means is None else means + stds = [(0.5, 0.5, 0.5)] if stds is None else stds + + # TIMM `data_cfg` Parameters + self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds + + # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values! + self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], [] + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + for idx in range(len(input_sizes)): + transform = timm.data.create_transform( + input_size=self.input_sizes[idx], + interpolation=self.interpolations[idx], + mean=self.means[idx], + std=self.stds[idx], + crop_pct=1.0, # Set to 1.0 to ignore cropping (initial Resize sets `input_size`) + crop_mode="center", # Default crop mode -- no-op when `crop_pct == 1.0` + is_training=False, # No image augmentations when loading the transform! + ) + + # [Validation] Ensure appropriate transform structure, expected sizes + if not ( + isinstance(transform, Compose) + and (len(transform.transforms) == 4) + and isinstance(transform.transforms[0], Resize) + and isinstance(transform.transforms[1], CenterCrop) + and isinstance(transform.transforms[2], ToTensor) + and isinstance(transform.transforms[3], Normalize) + and (transform.transforms[0].size == self.input_sizes[idx][-1]) + and (transform.transforms[1].size == self.input_sizes[idx][-2:]) + ): + raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`") + + # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute. + # => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`) + resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3] + self.tvf_resize_params.append( + { + "size": resize_t.size, + "interpolation": TVF.pil_modes_mapping[resize_t.interpolation], + "max_size": None, + "antialias": True, + } + ) + self.tvf_crop_params.append({"output_size": crop_t.size}) + self.tvf_normalize_params.append( + { + "mean": norm_t.mean.float().numpy().tolist(), + "std": norm_t.std.float().numpy().tolist(), + "inplace": False, + } + ) + self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None + + # Handle Prismatic `image_resize_strategy` + if self.image_resize_strategy == "resize-naive": + self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size) + elif self.image_resize_strategy == "letterbox": + self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]]) + elif self.image_resize_strategy == "resize-crop": + pass + else: + raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!") + + # Dispatch **kwargs to super() + super().__init__(**kwargs) + + def apply_transform(self, img: Image.Image) -> torch.Tensor: + """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])""" + if self.tvf_do_letterbox: + img = letterbox_pad_transform(img, self.tvf_letterbox_fill) + + # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side! + imgs_t = [] + for idx in range(len(self.input_sizes)): + img_idx = TVF.resize(img, **self.tvf_resize_params[idx]) + img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx]) + img_idx_t = TVF.to_tensor(img_idx) + img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx]) + imgs_t.append(img_idx_t) + + # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0 + img_t = torch.vstack(imgs_t) + + return img_t + + def preprocess( + self, + images: Union[Image.Image, List[Image.Image]], + return_tensors: Optional[Union[str, TensorType]] = None, + **_: str, + ) -> BatchFeature: + """ + Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we + explicitly only handle PIL.Image.Image instances for simplicity. + + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray + + @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values" + """ + if not isinstance(images, list): + images = [images] + + # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor + pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images]) + + # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert + return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors) + + def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature: + return self.preprocess(images, **kwargs) + + +# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer === +# =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py +class PrismaticProcessor(ProcessorMixin): + attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"] + image_processor_class: str = "AutoImageProcessor" + tokenizer_class: str = "AutoTokenizer" + + def __init__( + self, + image_processor: Optional[ImageProcessingMixin] = None, + tokenizer: Optional[PreTrainedTokenizerBase] = None, + ) -> None: + super().__init__(image_processor, tokenizer) + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], + images: Union[Image.Image, List[Image.Image]], + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Optional[Union[bool, str, TruncationStrategy]] = None, + max_length: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + ) -> BatchFeature: + """ + Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer, + forwards images to PrismaticImageProcessor. + + @param text: The (batch) of text to encode; must be a string or list of strings. + @param images: A (batch of) PIL.Image.Image instance(s) to preprocess. + @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False > + @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified + @param max_length: Maximum length (in tokens) to truncate + @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH) + + @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`. + """ + pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"] + text_inputs = self.tokenizer( + text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length + ) + + # [Validate] Need same number of images and text inputs! + if pixel_values.shape[0] != text_inputs.input_ids.shape[0]: + raise ValueError("Batch is malformed; expected same number of images and text inputs!") + + return BatchFeature(data={**text_inputs, "pixel_values": pixel_values}) + + # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation === + def batch_decode( + self, + sequences: Union[List[int], List[List[int]], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> List[str]: + return self.tokenizer.batch_decode( + sequences=sequences, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + def decode( + self, + token_ids: Union[int, List[int], torch.Tensor, Any], # `Any` = np.ndarray | tf.Tensor + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: Optional[bool] = None, + **kwargs: str, + ) -> str: + return self.tokenizer.decode( + token_ids=token_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def model_input_names(self) -> List[str]: + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/processor_config.json b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f892f8a8f53e2f3603a7cf86043a1add317c1787 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/processor_config.json @@ -0,0 +1,6 @@ +{ + "auto_map": { + "AutoProcessor": "processing_prismatic.PrismaticProcessor" + }, + "processor_class": "PrismaticProcessor" +} diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/proprio_projector--40000_checkpoint.pt b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/proprio_projector--40000_checkpoint.pt new file mode 100644 index 0000000000000000000000000000000000000000..c86960c4822c77e765f5c07f0fee474215fc7e94 --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/proprio_projector--40000_checkpoint.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5b4f393301f8fb0b800401cd8ed768b08c8fea8c3d6f998044dcdffe5eb1dd7d +size 67373488 diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--40000_chkpt/special_tokens_map.json 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100644 index 0000000000000000000000000000000000000000..5478446e8ad68ad86c328c69f0cf7e5b19911e9e --- /dev/null +++ b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--45000_chkpt/proprio_projector--45000_checkpoint.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:460af2188e1b50aa752260da5c48657fb37f48cc3b6563351baabfda16d349ac +size 67373488 diff --git a/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--45000_chkpt/special_tokens_map.json 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b/results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M50000-F5000-D20000--50000_chkpt/lora_adapter/README.md @@ -0,0 +1,202 @@ +--- +base_model: /inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/ai_models/openvla/openvla-7b +library_name: peft +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.11.1 \ No newline at end of file diff --git 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