diff --git a/configuration_florence2.py b/configuration_florence2.py index 622f749..f230565 100644 --- a/configuration_florence2.py +++ b/configuration_florence2.py @@ -249,6 +249,7 @@ class Florence2LanguageConfig(PretrainedConfig): self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True + self.forced_bos_token_id = bos_token_id super().__init__( num_labels=num_labels, @@ -262,12 +263,12 @@ class Florence2LanguageConfig(PretrainedConfig): ) # ensure backward compatibility for BART CNN models - if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): - self.forced_bos_token_id = self.bos_token_id - warnings.warn( - f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " - "The config can simply be saved and uploaded again to be fixed." - ) + # if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): + # self.forced_bos_token_id = self.bos_token_id + # warnings.warn( + # f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " + # "The config can simply be saved and uploaded again to be fixed." + # ) class Florence2Config(PretrainedConfig): r""" diff --git a/modeling_florence2.py b/modeling_florence2.py index 599f450..c10b8a0 100644 --- a/modeling_florence2.py +++ b/modeling_florence2.py @@ -61,6 +61,8 @@ from transformers.modeling_outputs import ( Seq2SeqLMOutput, Seq2SeqModelOutput, ) +import transformers +from packaging import version if is_flash_attn_2_available(): @@ -1934,7 +1936,8 @@ class Florence2Decoder(Florence2LanguagePreTrainedModel): class Florence2LanguageModel(Florence2LanguagePreTrainedModel): - _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: Florence2LanguageConfig): super().__init__(config) @@ -2057,7 +2060,8 @@ class Florence2LanguageModel(Florence2LanguagePreTrainedModel): class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel, GenerationMixin): base_model_prefix = "model" - _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] _keys_to_ignore_on_load_missing = ["final_logits_bias"] def __init__(self, config: Florence2LanguageConfig): @@ -2067,14 +2071,20 @@ class Florence2LanguageForConditionalGeneration(Florence2LanguagePreTrainedModel self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) # Initialize weights and apply final processing - self.post_init() - + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + self.post_init() + def _tie_weights(self): if self.config.tie_word_embeddings: self._tie_or_clone_weights(self.model.encoder.embed_tokens, self.model.shared) self._tie_or_clone_weights(self.model.decoder.embed_tokens, self.model.shared) self._tie_or_clone_weights(self.lm_head, self.model.shared) + def tie_weights(self): + self.model.encoder.embed_tokens.weight = self.model.shared.weight + self.model.decoder.embed_tokens.weight = self.model.shared.weight + self.lm_head.weight = self.model.shared.weight + def get_encoder(self): return self.model.get_encoder() @@ -2536,7 +2546,8 @@ class Florence2VisionModelWithProjection(Florence2PreTrainedModel): FLORENCE2_START_DOCSTRING, ) class Florence2ForConditionalGeneration(Florence2PreTrainedModel, GenerationMixin): - _tied_weights_keys = ["language_model.encoder.embed_tokens.weight", "language_model.decoder.embed_tokens.weight", "language_model.lm_head.weight"] + if not version.parse(transformers.__version__) >= version.parse('5.0.0'): + _tied_weights_keys = ["language_model.encoder.embed_tokens.weight", "language_model.decoder.embed_tokens.weight", "language_model.lm_head.weight"] def __init__(self, config: Florence2Config): super().__init__(config) assert config.vision_config.model_type == 'davit', 'only DaViT is supported for now' diff --git a/nodes.py b/nodes.py index 00b0190..f6017bb 100644 --- a/nodes.py +++ b/nodes.py @@ -4,7 +4,7 @@ import torchvision.transforms.functional as F import io import os import matplotlib -matplotlib.use('Agg') +matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as patches from PIL import Image, ImageDraw, ImageColor, ImageFont @@ -13,14 +13,69 @@ import numpy as np import re from pathlib import Path -#workaround for unnecessary flash_attn requirement -from unittest.mock import patch from transformers.dynamic_module_utils import get_imports - import transformers +from packaging import version from safetensors.torch import save_file +def load_model(model_path: str, attention: str, dtype: torch.dtype, offload_device: torch.device): + from .modeling_florence2 import Florence2ForConditionalGeneration, Florence2Config + from transformers import CLIPImageProcessor, BartTokenizerFast + from .processing_florence2 import Florence2Processor + from accelerate import init_empty_weights + from accelerate.utils import set_module_tensor_to_device + + config = Florence2Config.from_pretrained(model_path) + config._attn_implementation = attention + with init_empty_weights(): + model = Florence2ForConditionalGeneration(config) + + checkpoint_path = os.path.join(model_path, "model.safetensors") + if not os.path.exists(checkpoint_path): + checkpoint_path = os.path.join(model_path, "pytorch_model.bin") + if os.path.exists(checkpoint_path): + state_dict = load_torch_file(checkpoint_path) + else: + raise FileNotFoundError(f"No model weights found at {model_path}") + + key_mapping = {} + if "language_model.model.shared.weight" in state_dict: + key_mapping["language_model.model.encoder.embed_tokens.weight"] = "language_model.model.shared.weight" + key_mapping["language_model.model.decoder.embed_tokens.weight"] = "language_model.model.shared.weight" + + for name, param in model.named_parameters(): + # Check if we need to remap the key + actual_key = key_mapping.get(name, name) + + if actual_key in state_dict: + set_module_tensor_to_device(model, name, offload_device, value=state_dict[actual_key].to(dtype)) + else: + print(f"Parameter {name} not found in state_dict.") + + # Tie embeddings + model.language_model.tie_weights() + model = model.eval().to(dtype).to(offload_device) + + # Create image processor + image_processor = CLIPImageProcessor( + do_resize=True, + size={"height": 768, "width": 768}, + resample=3, # BICUBIC + do_center_crop=False, + do_rescale=True, + rescale_factor=1/255.0, + do_normalize=True, + image_mean=[0.485, 0.456, 0.406], + image_std=[0.229, 0.224, 0.225], + ) + image_processor.image_seq_length = 577 + + # Create tokenizer - Florence2 uses BART tokenizer + tokenizer = BartTokenizerFast.from_pretrained(model_path) + processor = Florence2Processor(image_processor=image_processor, tokenizer=tokenizer) + return model, processor + def fixed_get_imports(filename: str | os.PathLike) -> list[str]: try: if not str(filename).endswith("modeling_florence2.py"): @@ -55,7 +110,11 @@ def create_path_dict(paths: list[str], predicate: Callable[[Path], bool] = lambd import comfy.model_management as mm -from comfy.utils import ProgressBar +from comfy.utils import ProgressBar, load_torch_file + +device = mm.get_torch_device() +offload_device = mm.unet_offload_device() + import folder_paths script_directory = os.path.dirname(os.path.abspath(__file__)) @@ -65,7 +124,7 @@ os.makedirs(model_directory, exist_ok=True) # Ensure ComfyUI knows about the LLM model path folder_paths.add_model_folder_path("LLM", model_directory) -from transformers import AutoModelForCausalLM, AutoProcessor, set_seed +from transformers import AutoProcessor, set_seed model_list = [ 'microsoft/Florence-2-base', @@ -113,22 +172,21 @@ class DownloadAndLoadFlorence2Model: def loadmodel(self, model, precision, attention, lora=None, convert_to_safetensors=False): if model not in model_list: raise ValueError(f"Model {model} is not in the supported model list.") - device = mm.get_torch_device() - offload_device = mm.unet_offload_device() + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] model_name = model.rsplit('/', 1)[-1] model_path = os.path.join(model_directory, model_name) - + if not os.path.exists(model_path): print(f"Downloading Florence2 model to: {model_path}") from huggingface_hub import snapshot_download snapshot_download(repo_id=model, local_dir=model_path, local_dir_use_symlinks=False) - + print(f"Florence2 using {attention} for attention") - + if convert_to_safetensors: model_weight_path = os.path.join(model_path, 'pytorch_model.bin') if os.path.exists(model_weight_path): @@ -144,29 +202,27 @@ class DownloadAndLoadFlorence2Model: print(f"Conversion successful. Deleting original file: {model_weight_path}") os.remove(model_weight_path) print(f"Original {model_weight_path} file deleted.") - - if transformers.__version__ < '4.51.0': - with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports): #workaround for unnecessary flash_attn requirement - model = AutoModelForCausalLM.from_pretrained(model_path, attn_implementation=attention, torch_dtype=dtype,trust_remote_code=True).to(offload_device) + + if version.parse(transformers.__version__) >= version.parse('5.0.0'): + model, processor = load_model(model_path, attention, dtype, offload_device) else: from .modeling_florence2 import Florence2ForConditionalGeneration - model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, torch_dtype=dtype).to(offload_device) - - processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) + model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, dtype=dtype).to(offload_device) + processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) if lora is not None: from peft import PeftModel adapter_name = lora model = PeftModel.from_pretrained(model, adapter_name, trust_remote_code=True) - + florence2_model = { - 'model': model, + 'model': model, 'processor': processor, 'dtype': dtype } return (florence2_model,) - + class DownloadAndLoadFlorence2Lora: @classmethod def INPUT_TYPES(s): @@ -227,8 +283,6 @@ class Florence2ModelLoader: CATEGORY = "Florence2" def loadmodel(self, model, precision, attention, lora=None, convert_to_safetensors=False): - device = mm.get_torch_device() - offload_device = mm.unet_offload_device() dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] model_path = Florence2ModelLoader.model_paths.get(model) print(f"Loading model from {model_path}") @@ -249,21 +303,20 @@ class Florence2ModelLoader: os.remove(model_weight_path) print(f"Original {model_weight_path} file deleted.") - if transformers.__version__ < '4.51.0': - with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports): #workaround for unnecessary flash_attn requirement - model = AutoModelForCausalLM.from_pretrained(model_path, attn_implementation=attention, torch_dtype=dtype,trust_remote_code=True).to(offload_device) + if version.parse(transformers.__version__) >= version.parse('5.0.0'): + model, processor = load_model(model_path, attention, dtype, offload_device) else: from .modeling_florence2 import Florence2ForConditionalGeneration - model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, torch_dtype=dtype).to(offload_device) - processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) + model = Florence2ForConditionalGeneration.from_pretrained(model_path, attn_implementation=attention, dtype=dtype).to(offload_device) + processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) if lora is not None: from peft import PeftModel adapter_name = lora model = PeftModel.from_pretrained(model, adapter_name, trust_remote_code=True) - + florence2_model = { - 'model': model, + 'model': model, 'processor': processor, 'dtype': dtype } @@ -327,16 +380,14 @@ class Florence2Run: def encode(self, image, text_input, florence2_model, task, fill_mask, keep_model_loaded=False, num_beams=3, max_new_tokens=1024, do_sample=True, output_mask_select="", seed=None): - device = mm.get_torch_device() _, height, width, _ = image.shape - offload_device = mm.unet_offload_device() annotated_image_tensor = None mask_tensor = None processor = florence2_model['processor'] model = florence2_model['model'] dtype = florence2_model['dtype'] model.to(device) - + if seed: set_seed(self.hash_seed(seed)) @@ -371,7 +422,7 @@ class Florence2Run: prompt = task_prompt image = image.permute(0, 3, 1, 2) - + out = [] out_masks = [] out_results = [] diff --git a/processing_florence2.py b/processing_florence2.py new file mode 100644 index 0000000..f368b71 --- /dev/null +++ b/processing_florence2.py @@ -0,0 +1,1053 @@ +# coding=utf-8 +# Copyright 2024 Microsoft and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for Florence-2. +""" + +import re +import logging +from typing import List, Optional, Union +import numpy as np + +import torch + +from transformers.feature_extraction_utils import BatchFeature +from transformers.image_utils import ImageInput, is_valid_image +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils_base import ( + PaddingStrategy, + PreTokenizedInput, + TextInput, + TruncationStrategy, +) +from transformers.utils import TensorType + + +logger = logging.getLogger(__name__) + +# Copied from transformers.models.idefics2.processing_idefics2.is_url +def is_url(val) -> bool: + return isinstance(val, str) and val.startswith("http") + +# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url +def is_image_or_image_url(elem): + return is_url(elem) or is_valid_image(elem) + + +def _is_str_or_image(elem): + return isinstance(elem, (str)) or is_image_or_image_url(elem) + + +class Florence2Processor(ProcessorMixin): + r""" + Constructs a Florence2 processor which wraps a Florence2 image processor and a Florence2 tokenizer into a single processor. + + [`Florence2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BartTokenizerFast`]. See the + [`~Florence2Processor.__call__`] and [`~Florence2Processor.decode`] for more information. + + Args: + image_processor ([`CLIPImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`BartTokenizerFast`], *optional*): + The tokenizer is a required input. + """ + + attributes = ["image_processor", "tokenizer"] + image_processor_class = "CLIPImageProcessor" + tokenizer_class = ("BartTokenizer", "BartTokenizerFast") + + def __init__( + self, + image_processor=None, + tokenizer=None, + ): + if image_processor is None: + raise ValueError("You need to specify an `image_processor`.") + if tokenizer is None: + raise ValueError("You need to specify a `tokenizer`.") + if not hasattr(image_processor, "image_seq_length"): + raise ValueError("Image processor is missing an `image_seq_length` attribute.") + + self.image_seq_length = image_processor.image_seq_length + + # Get existing additional_special_tokens safely (works with both Roberta and BART tokenizers) + existing_special_tokens = list(getattr(tokenizer, 'additional_special_tokens', []) or []) + + tokens_to_add = { + 'additional_special_tokens': \ + existing_special_tokens + \ + ['', '', '', ''] + \ + [f'' for x in range(1000)] + \ + ['', '', '', '','', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''] + } + tokenizer.add_special_tokens(tokens_to_add) + + self.tasks_answer_post_processing_type = { + '': 'pure_text', + '': 'ocr', + '': 'pure_text', + '': 'pure_text', + '': 'pure_text', + '': 'description_with_bboxes', + '': 'description_with_bboxes', + '': "phrase_grounding", + '': 'polygons', + '': 'polygons', + '': 'description_with_bboxes_or_polygons', + '': 'pure_text', + '': 'pure_text', + '': 'pure_text', + '': 'bboxes' + } + + self.task_prompts_without_inputs = { + '': 'What is the text in the image?', + '': 'What is the text in the image, with regions?', + '': 'What does the image describe?', + '': 'Describe in detail what is shown in the image.', + '': 'Describe with a paragraph what is shown in the image.', + '': 'Locate the objects with category name in the image.', + '': 'Locate the objects in the image, with their descriptions.', + '': 'Locate the region proposals in the image.' + } + + self.task_prompts_with_input = { + '': "Locate the phrases in the caption: {input}", + '': 'Locate {input} in the image with mask', + '': 'What is the polygon mask of region {input}', + '': 'Locate {input} in the image.', + '': 'What is the region {input}?', + '': 'What does the region {input} describe?', + '': 'What text is in the region {input}?', + } + + self.post_processor = Florence2PostProcesser(tokenizer=tokenizer) + + + super().__init__(image_processor, tokenizer) + + def _construct_prompts(self, text): + # replace the task tokens with the task prompts if task token is in the text + prompts = [] + for _text in text: + # 1. fixed task prompts without additional inputs + for task_token, task_prompt in self.task_prompts_without_inputs.items(): + if task_token in _text: + assert _text == task_token, f"Task token {task_token} should be the only token in the text." + _text = task_prompt + break + # 2. task prompts with additional inputs + for task_token, task_prompt in self.task_prompts_with_input.items(): + if task_token in _text: + _text = task_prompt.format(input=_text.replace(task_token, '')) + break + prompts.append(_text) + return prompts + + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + images: ImageInput = None, + tokenize_newline_separately: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length=None, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + do_resize: bool = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821 + input_data_format: Optional[ + Union[str, "ChannelDimension"] # noqa: F821 + ] = None, + resample: "PILImageResampling" = None, # noqa: F821 + do_convert_rgb: bool = None, + do_thumbnail: bool = None, + do_align_long_axis: bool = None, + do_rescale: bool = None, + ) -> BatchFeature: + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` + and `kwargs` arguments to BartTokenizerFast's [`~BartTokenizerFast.__call__`] if `text` is not `None` to encode + the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to + CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring + of the above two methods for more information. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a + number of channels, H and W are image height and width. + tokenize_newline_separately (`bool`, defaults to `True`): + Adds a separately tokenized '\n' at the end of the prompt. + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + truncation (`bool`, *optional*): + Activates truncation to cut input sequences longer than `max_length` to `max_length`. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` + is provided, the `input_ids` will also contain the suffix input ids. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **labels** -- Labels compatible with training if `suffix` is not None + """ + + return_token_type_ids = False + + if images is None: + raise ValueError("`images` are expected as arguments to a `Florence2Processor` instance.") + if text is None: + logger.warning_once( + "You are using Florence-2 without a text prompt." + ) + text = "" + + if isinstance(text, List) and isinstance(images, List): + if len(images) < len(text): + raise ValueError( + f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image." + ) + if _is_str_or_image(text): + text = [text] + elif isinstance(text, list) and _is_str_or_image(text[0]): + pass + + pixel_values = self.image_processor( + images, + do_resize=do_resize, + do_normalize=do_normalize, + return_tensors=return_tensors, + image_mean=image_mean, + image_std=image_std, + input_data_format=input_data_format, + data_format=data_format, + resample=resample, + do_convert_rgb=do_convert_rgb, + )["pixel_values"] + + if max_length is not None: + max_length -= self.image_seq_length # max_length has to account for the image tokens + + text = self._construct_prompts(text) + + inputs = self.tokenizer( + text, + return_tensors=return_tensors, + padding=padding, + max_length=max_length, + truncation=truncation, + return_token_type_ids=return_token_type_ids, + ) + + return_data = {**inputs, "pixel_values": pixel_values} + + if return_token_type_ids: + labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100) + return_data.update({"labels": labels}) + return BatchFeature(data=return_data) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Florence2 + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Florence2 + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Florence2 + def model_input_names(self): + 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)) + + def post_process_generation(self, text, task, image_size): + """ + Post-process the output of the model to each of the task outputs. + + Args: + text (`str`): The text to post-process. + task (`str`): The task to post-process the text for. + image_size (`Tuple[int, int]`): The size of the image. height x width. + """ + + task_answer_post_processing_type = self.tasks_answer_post_processing_type.get(task, 'pure_text') + task_answer = self.post_processor( + text=text, + image_size=image_size, + parse_tasks=task_answer_post_processing_type, + )[task_answer_post_processing_type] + + if task_answer_post_processing_type == 'pure_text': + final_answer = task_answer + # remove the special tokens + final_answer = final_answer.replace('', '').replace('', '') + elif task_answer_post_processing_type in ['od', 'description_with_bboxes', 'bboxes']: + od_instances = task_answer + bboxes_od = [_od_instance['bbox'] for _od_instance in od_instances] + labels_od = [str(_od_instance['cat_name']) for _od_instance in od_instances] + final_answer = {'bboxes': bboxes_od, 'labels': labels_od} + elif task_answer_post_processing_type in ['ocr']: + bboxes = [_od_instance['quad_box'] for _od_instance in task_answer] + labels = [str(_od_instance['text']) for _od_instance in task_answer] + final_answer = {'quad_boxes': bboxes, 'labels': labels} + elif task_answer_post_processing_type in ['phrase_grounding']: + bboxes = [] + labels = [] + for _grounded_phrase in task_answer: + for _bbox in _grounded_phrase['bbox']: + bboxes.append(_bbox) + labels.append(_grounded_phrase['cat_name']) + final_answer = {'bboxes': bboxes, 'labels': labels} + elif task_answer_post_processing_type in ['description_with_polygons', 'polygons']: + labels = [] + polygons = [] + for result in task_answer: + label = result['cat_name'] + _polygons = result['polygons'] + labels.append(label) + polygons.append(_polygons) + final_answer = {'polygons': polygons, 'labels': labels} + elif task_answer_post_processing_type in ['description_with_bboxes_or_polygons']: + bboxes = [] + bboxes_labels = [] + polygons = [] + polygons_labels = [] + for result in task_answer: + label = result['cat_name'] + if 'polygons' in result: + _polygons = result['polygons'] + polygons.append(_polygons) + polygons_labels.append(label) + else: + _bbox = result['bbox'] + bboxes.append(_bbox) + bboxes_labels.append(label) + final_answer = {'bboxes': bboxes, 'bboxes_labels': bboxes_labels, 'polygons': polygons, 'polygons_labels': polygons_labels} + else: + raise ValueError('Unknown task answer post processing type: {}'.format(task_answer_post_processing_type)) + + final_answer = { + task: final_answer} + return final_answer + +class BoxQuantizer(object): + def __init__(self, mode, bins): + self.mode = mode + self.bins = bins + + def quantize(self, boxes: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + quantized_xmin = ( + xmin / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_ymin = ( + ymin / size_per_bin_h).floor().clamp(0, bins_h - 1) + quantized_xmax = ( + xmax / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_ymax = ( + ymax / size_per_bin_h).floor().clamp(0, bins_h - 1) + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + quantized_boxes = torch.cat( + (quantized_xmin, quantized_ymin, quantized_xmax, quantized_ymax), dim=-1 + ).int() + + return quantized_boxes + + def dequantize(self, boxes: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + # Add 0.5 to use the center position of the bin as the coordinate. + dequantized_xmin = (xmin + 0.5) * size_per_bin_w + dequantized_ymin = (ymin + 0.5) * size_per_bin_h + dequantized_xmax = (xmax + 0.5) * size_per_bin_w + dequantized_ymax = (ymax + 0.5) * size_per_bin_h + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + dequantized_boxes = torch.cat( + (dequantized_xmin, dequantized_ymin, + dequantized_xmax, dequantized_ymax), dim=-1 + ) + + return dequantized_boxes + + +class CoordinatesQuantizer(object): + """ + Quantize coornidates (Nx2) + """ + + def __init__(self, mode, bins): + self.mode = mode + self.bins = bins + + def quantize(self, coordinates: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)' + x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + quantized_x = (x / size_per_bin_w).floor().clamp(0, bins_w - 1) + quantized_y = (y / size_per_bin_h).floor().clamp(0, bins_h - 1) + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + quantized_coordinates = torch.cat( + (quantized_x, quantized_y), dim=-1 + ).int() + + return quantized_coordinates + + def dequantize(self, coordinates: torch.Tensor, size): + bins_w, bins_h = self.bins # Quantization bins. + size_w, size_h = size # Original image size. + size_per_bin_w = size_w / bins_w + size_per_bin_h = size_h / bins_h + assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)' + x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1]. + + if self.mode == 'floor': + # Add 0.5 to use the center position of the bin as the coordinate. + dequantized_x = (x + 0.5) * size_per_bin_w + dequantized_y = (y + 0.5) * size_per_bin_h + + elif self.mode == 'round': + raise NotImplementedError() + + else: + raise ValueError('Incorrect quantization type.') + + dequantized_coordinates = torch.cat( + (dequantized_x, dequantized_y), dim=-1 + ) + + return dequantized_coordinates + + +class Florence2PostProcesser(object): + """ + Florence-2 post process for converting text prediction to various tasks results. + + Args: + config: A dict of configs. + tokenizer: A tokenizer for decoding text to spans. + sample config: + UNIFIED_POST_PROCESS: + # commom configs + NUM_BBOX_HEIGHT_BINS: 1000 + NUM_BBOX_WIDTH_BINS: 1000 + COORDINATES_HEIGHT_BINS: 1000 + COORDINATES_WIDTH_BINS: 1000 + # task specific configs, override the common configs + PRASE_TASKS: + - TASK_NAME: 'video_dense_caption' + PATTERN: 'r([a-zA-Z0-9 ]+)' + SCORE_MODE: 'avg_cat_name_scores' + NUM_BINS: 100 + - TASK_NAME: 'od' + PATTERN: 'r([a-zA-Z0-9 ]+)' + SCORE_MODE: 'avg_cat_name_scores' + + Returns: + parsed_dict (dict): A dict of parsed results. + """ + def __init__( + self, + tokenizer=None + ): + parse_tasks = [] + parse_task_configs = {} + config = self._create_default_config() + for task in config['PARSE_TASKS']: + parse_tasks.append(task['TASK_NAME']) + parse_task_configs[task['TASK_NAME']] = task + + self.config = config + self.parse_tasks = parse_tasks + self.parse_tasks_configs = parse_task_configs + + self.tokenizer = tokenizer + if self.tokenizer is not None: + self.all_special_tokens = set(self.tokenizer.all_special_tokens) + + self.init_quantizers() + self.black_list_of_phrase_grounding = self._create_black_list_of_phrase_grounding() + + def _create_black_list_of_phrase_grounding(self): + black_list = {} + + if 'phrase_grounding' in self.parse_tasks and self.parse_tasks_configs['phrase_grounding']['FILTER_BY_BLACK_LIST']: + black_list = set( + ['it', 'I', 'me', 'mine', + 'you', 'your', 'yours', + 'he', 'him', 'his', + 'she', 'her', 'hers', + 'they', 'them', 'their', 'theirs', + 'one', 'oneself', + 'we', 'us', 'our', 'ours', + 'you', 'your', 'yours', + 'they', 'them', 'their', 'theirs', + 'mine', 'yours', 'his', 'hers', 'its', + 'ours', 'yours', 'theirs', + 'myself', 'yourself', 'himself', 'herself', 'itself', + 'ourselves', 'yourselves', 'themselves', + 'this', 'that', + 'these', 'those', + 'who', 'whom', 'whose', 'which', 'what', + 'who', 'whom', 'whose', 'which', 'that', + 'all', 'another', 'any', 'anybody', 'anyone', 'anything', + 'each', 'everybody', 'everyone', 'everything', + 'few', 'many', 'nobody', 'none', 'one', 'several', + 'some', 'somebody', 'someone', 'something', + 'each other', 'one another', + 'myself', 'yourself', 'himself', 'herself', 'itself', + 'ourselves', 'yourselves', 'themselves', + 'the image', 'image', 'images', 'the', 'a', 'an', 'a group', + 'other objects', 'lots', 'a set', + ] + ) + + return black_list + + def _create_default_config(self): + config = { + 'NUM_BBOX_HEIGHT_BINS': 1000, + 'NUM_BBOX_WIDTH_BINS': 1000, + 'BOX_QUANTIZATION_MODE': 'floor', + 'COORDINATES_HEIGHT_BINS': 1000, + 'COORDINATES_WIDTH_BINS': 1000, + 'COORDINATES_QUANTIZATION_MODE': 'floor', + 'PARSE_TASKS': [ + { + 'TASK_NAME': 'od', + 'PATTERN': r'([a-zA-Z0-9 ]+)' + }, + { + 'TASK_NAME': 'ocr', + 'PATTERN': r'(.+?)', + 'AREA_THRESHOLD': 0.00 + }, + { + 'TASK_NAME': 'phrase_grounding', + 'FILTER_BY_BLACK_LIST': True + }, + { + 'TASK_NAME': 'pure_text', + }, + { + 'TASK_NAME': 'description_with_bboxes', + }, + { + 'TASK_NAME': 'description_with_polygons', + }, + { + 'TASK_NAME': 'polygons', + }, + { + 'TASK_NAME': 'bboxes', + }, + { + 'TASK_NAME': 'description_with_bboxes_or_polygons', + } + ] + } + + return config + + def init_quantizers(self): + # we have box_quantizer (od, grounding) and coordinates_quantizer (ocr, referring_segmentation) + num_bbox_height_bins = self.config.get('NUM_BBOX_HEIGHT_BINS', 1000) + num_bbox_width_bins = self.config.get('NUM_BBOX_WIDTH_BINS', 1000) + box_quantization_mode = self.config.get('BOX_QUANTIZATION_MODE', 'floor') + self.box_quantizer = BoxQuantizer( + box_quantization_mode, + (num_bbox_width_bins, num_bbox_height_bins), + ) + + num_bbox_height_bins = self.config['COORDINATES_HEIGHT_BINS'] if 'COORDINATES_HEIGHT_BINS' in self.config else self.config.get('NUM_BBOX_HEIGHT_BINS', 1000) + num_bbox_width_bins = self.config['COORDINATES_WIDTH_BINS'] if 'COORDINATES_WIDTH_BINS' in self.config else self.config.get('NUM_BBOX_WIDTH_BINS', 1000) + box_quantization_mode = self.config.get('COORDINATES_QUANTIZATION_MODE') if 'COORDINATES_QUANTIZATION_MODE' in self.config else self.config.get('BOX_QUANTIZATION_MODE', 'floor') + self.coordinates_quantizer = CoordinatesQuantizer( + box_quantization_mode, + (num_bbox_width_bins, num_bbox_height_bins), + ) + + + def parse_od_from_text_and_spans( + self, + text, + pattern, + image_size, + phrase_centric=False + ): + parsed = list(re.finditer(pattern, text)) + + instances = [] + for i in range(len(parsed)): + # Prepare instance. + instance = {} + + if phrase_centric: + bbox_bins = [int(parsed[i].group(j)) for j in range(2, 6)] + else: + bbox_bins = [int(parsed[i].group(j)) for j in range(1, 5)] + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + if phrase_centric: + instance['cat_name'] = parsed[i].group(1).lower().strip() + else: + instance['cat_name'] = parsed[i].group(5).lower().strip() + instances.append(instance) + + return instances + + def parse_ocr_from_text_and_spans(self, + text, + pattern, + image_size, + area_threshold=-1.0, + ): + bboxes = [] + labels = [] + text = text.replace('', '') + # ocr with regions + parsed = re.findall(pattern, text) + instances = [] + image_width, image_height = image_size + + for ocr_line in parsed: + ocr_content = ocr_line[0] + quad_box = ocr_line[1:] + quad_box = [int(i) for i in quad_box] + quad_box = self.coordinates_quantizer.dequantize( + torch.tensor(np.array(quad_box).reshape(-1, 2)), + size=image_size + ).reshape(-1).tolist() + + if area_threshold > 0: + x_coords = [i for i in quad_box[0::2]] + y_coords = [i for i in quad_box[1::2]] + + # apply the Shoelace formula + area = 0.5 * abs(sum(x_coords[i] * y_coords[i + 1] - x_coords[i + 1] * y_coords[i] for i in range(4 - 1))) + + if area < (image_width * image_height) * area_threshold: + continue + + bboxes.append(quad_box) + labels.append(ocr_content) + instances.append({ + 'quad_box': quad_box, + 'text': ocr_content, + }) + return instances + + def parse_phrase_grounding_from_text_and_spans(self, text, pattern, image_size): + # ignore and + cur_span = 0 + if text.startswith(''): + cur_span += 3 + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + pattern = r"([^<]+(?:){4,})" + phrases = re.findall(pattern, text) + + # pattern should be text pattern and od pattern + pattern = r'^\s*(.*?)(?=||||||' + + instances = [] + for pharse_text in phrases: + phrase_text_strip = pharse_text.replace('', '', 1) + phrase_text_strip = pharse_text.replace('', '', 1) + + if phrase_text_strip == '': + cur_span += len(pharse_text) + continue + + # Prepare instance. + instance = {} + + # parse phrase, get string + phrase = re.search(pattern, phrase_text_strip) + if phrase is None: + cur_span += len(pharse_text) + continue + + # parse bboxes by box_pattern + bboxes_parsed = list(re.finditer(box_pattern, pharse_text)) + if len(bboxes_parsed) == 0: + cur_span += len(pharse_text) + continue + + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + if phrase in self.black_list_of_phrase_grounding: + cur_span += len(pharse_text) + continue + + # a list of list + bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed] + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + # exclude non-ascii characters + phrase = phrase.encode('ascii',errors='ignore').decode('ascii') + instance['cat_name'] = phrase + + instances.append(instance) + + return instances + + def parse_description_with_bboxes_from_text_and_spans(self, text, pattern, image_size, allow_empty_phrase=False): + # temporary parse solution, split by '.' + # ignore and + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + if allow_empty_phrase: + pattern = r"(?:(?:){{4,}})" + else: + pattern = r"([^<]+(?:){4,})" + phrases = re.findall(pattern, text) + + # pattern should be text pattern and od pattern + pattern = r'^\s*(.*?)(?=||||||' + + instances = [] + for pharse_text in phrases: + phrase_text_strip = pharse_text.replace('', '', 1) + phrase_text_strip = pharse_text.replace('', '', 1) + + if phrase_text_strip == '' and not allow_empty_phrase: + continue + + # parse phrase, get string + phrase = re.search(pattern, phrase_text_strip) + if phrase is None: + continue + + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + # parse bboxes by box_pattern + bboxes_parsed = list(re.finditer(box_pattern, pharse_text)) + if len(bboxes_parsed) == 0: + continue + + # a list of list + bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed] + + bboxes = self.box_quantizer.dequantize( + boxes=torch.tensor(bbox_bins), + size=image_size + ).tolist() + + phrase = phrase.encode('ascii',errors='ignore').decode('ascii') + for _bboxes in bboxes: + # Prepare instance. + instance = {} + instance['bbox'] = _bboxes + # exclude non-ascii characters + instance['cat_name'] = phrase + instances.append(instance) + + return instances + + def parse_description_with_polygons_from_text_and_spans(self, text, pattern, image_size, + allow_empty_phrase=False, + polygon_sep_token='', + polygon_start_token='', + polygon_end_token='', + with_box_at_start=False, + ): + + # ref_seg format: '<><><><><><>' + # ignore and + + text = text.replace('', '') + text = text.replace('', '') + text = text.replace('', '') + + if allow_empty_phrase: + pattern = rf"(?:(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})" + else: + # [^<]+: This part matches one or more characters that are not the < symbol. + # The ^ inside the square brackets [] is a negation, meaning it matches anything except <. + # + pattern = rf"([^<]+(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})" + phrases = re.findall(pattern, text) + + phrase_string_pattern = r'^\s*(.*?)(?=||||||)' + box_pattern = rf'((?:)+)(?:{re.escape(polygon_sep_token)}|$)' + + # one polygons instance is separated by polygon_start_token and polygon_end_token + polygons_instance_pattern = rf'{re.escape(polygon_start_token)}(.*?){re.escape(polygon_end_token)}' + + instances = [] + for phrase_text in phrases: + + # exclude loc_\d+> + # need to get span if want to include category score + phrase_text_strip = re.sub(r'^loc_\d+>', '', phrase_text, count=1) + + # phrase = phrase.replace('', '') + # phrase = phrase.replace('poly>', '') + + if phrase_text_strip == '' and not allow_empty_phrase: + continue + + + # parse phrase, get string + phrase = re.search(phrase_string_pattern, phrase_text_strip) + if phrase is None: + continue + phrase = phrase.group() + # remove leading and trailing spaces + phrase = phrase.strip() + + # parse bboxes by box_pattern + + # split by polygon_start_token and polygon_end_token first using polygons_instance_pattern + if polygon_start_token in phrase_text and polygon_end_token in phrase_text: + polygons_instances_parsed = list(re.finditer(polygons_instance_pattern, phrase_text)) + else: + polygons_instances_parsed = [phrase_text] + + for _polygons_instances_parsed in polygons_instances_parsed: + # Prepare instance. + instance = {} + + # polygons_parsed= list(re.finditer(box_pattern, phrase_text)) + if isinstance(_polygons_instances_parsed, str): + polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed)) + else: + polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed.group(1))) + if len(polygons_parsed) == 0: + continue + + # a list of list (polygon) + bbox = [] + polygons = [] + for _polygon_parsed in polygons_parsed: + # group 1: whole ... + _polygon = _polygon_parsed.group(1) + # parse into list of int + _polygon = [int(_loc_parsed.group(1)) for _loc_parsed in re.finditer(r'', _polygon)] + if with_box_at_start and len(bbox) == 0: + if len(_polygon) > 4: + # no valid bbox prediction + bbox = _polygon[:4] + _polygon = _polygon[4:] + else: + bbox = [0, 0, 0, 0] + # abandon last element if is not paired + if len(_polygon) % 2 == 1: + _polygon = _polygon[:-1] + + # reshape into (n, 2) + _polygon = self.coordinates_quantizer.dequantize( + torch.tensor(np.array(_polygon).reshape(-1, 2)), + size=image_size + ).reshape(-1).tolist() + # reshape back + polygons.append(_polygon) + + instance['cat_name'] = phrase + instance['polygons'] = polygons + if len(bbox) != 0: + instance['bbox'] = self.box_quantizer.dequantize( + boxes=torch.tensor([bbox]), + size=image_size + ).tolist()[0] + + instances.append(instance) + + return instances + + def __call__( + self, + text=None, + image_size=None, + parse_tasks=None, + ): + """ + Args: + text: model outputs + image_size: (width, height) + parse_tasks: a list of tasks to parse, if None, parse all tasks. + + """ + if parse_tasks is not None: + if isinstance(parse_tasks, str): + parse_tasks = [parse_tasks] + for _parse_task in parse_tasks: + assert _parse_task in self.parse_tasks, f'parse task {_parse_task} not supported' + + # sequence or text should be provided + assert text is not None, 'text should be provided' + + parsed_dict = { + 'text': text + } + + for task in self.parse_tasks: + if parse_tasks is not None and task not in parse_tasks: + continue + + pattern = self.parse_tasks_configs[task].get('PATTERN', None) + + if task == 'ocr': + instances = self.parse_ocr_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + area_threshold=self.parse_tasks_configs[task].get('AREA_THRESHOLD', 0.0), + ) + parsed_dict['ocr'] = instances + elif task == 'phrase_grounding': + instances = self.parse_phrase_grounding_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['phrase_grounding'] = instances + elif task == 'pure_text': + parsed_dict['pure_text'] = text + elif task == 'description_with_bboxes': + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_bboxes'] = instances + elif task == 'description_with_polygons': + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_polygons'] = instances + elif task == 'polygons': + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + allow_empty_phrase=True, + ) + parsed_dict['polygons'] = instances + elif task == 'bboxes': + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + allow_empty_phrase=True, + ) + parsed_dict['bboxes'] = instances + elif task == 'description_with_bboxes_or_polygons': + if '' in text: + # only support either polygons or bboxes, not both at the same time + instances = self.parse_description_with_polygons_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + else: + instances = self.parse_description_with_bboxes_from_text_and_spans( + text, + pattern=pattern, + image_size=image_size, + ) + parsed_dict['description_with_bboxes_or_polygons'] = instances + else: + raise ValueError("task {} is not supported".format(task)) + + return parsed_dict