# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # # 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. import os import numpy as np import paddle from PIL import Image from ..transformers import AutoModel, AutoProcessor from ..utils.log import logger from .task import Task from .utils import dygraph_mode_guard, static_mode_guard usage = r""" from paddlenlp import Taskflow from PIL import Image # Multi modal feature_extraction with ernie_vil-2.0-base-zh vision_language = Taskflow("feature_extraction", model='PaddlePaddle/ernie_vil-2.0-base-zh') image_embeds = vision_language([Image.open("demo/000000039769.jpg")]) print(image_embeds) ''' Tensor(shape=[1, 768], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[-0.59475428, -0.69795364, 0.22144008, 0.88066685, -0.58184201, -0.73454666, 0.95557910, -0.61410815, 0.23474170, 0.13301648, 0.86196446, 0.12281934, 0.69097638, 1.47614217, 0.07238606, ... ''' text_embeds = vision_language(["猫的照片","狗的照片"]) text_features = text_embeds["features"] print(text_features) ''' Tensor(shape=[2, 768], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[ 0.04250504, -0.41429776, 0.26163983, ..., 0.26221892, 0.34387422, 0.18779707], ''' image_features /= image_features.norm(axis=-1, keepdim=True) text_features /= text_features.norm(axis=-1, keepdim=True) logits_per_image = 100 * image_features @ text_features.t() probs = F.softmax(logits_per_image, axis=-1) print(probs) ''' Tensor(shape=[1, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, [[0.99833173, 0.00166824]]) ''' """ class MultimodalFeatureExtractionTask(Task): """ Feature extraction task using no model head. This task extracts the hidden states from the base model, which can be used as features in retrieval and clustering tasks. Args: task(string): The name of task. model(string): The model name in the task. kwargs (dict, optional): Additional keyword arguments passed along to the specific task. """ resource_files_names = { "model_state": "model_state.pdparams", "config": "config.json", "vocab_file": "vocab.txt", "preprocessor_config": "preprocessor_config.json", "special_tokens_map": "special_tokens_map.json", "tokenizer_config": "tokenizer_config.json", } resource_files_urls = { "PaddlePaddle/ernie_vil-2.0-base-zh": { "model_state": [ "https://paddlenlp.bj.bcebos.com/models/community/PaddlePaddle/ernie_vil-2.0-base-zh/model_state.pdparams", "38d8c8e01f74ba881e87d9a3f669e5ae", ], "config": [ "https://paddlenlp.bj.bcebos.com/models/community/PaddlePaddle/ernie_vil-2.0-base-zh/config.json", "caf929b450d5638e8df2a95c936519e7", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/models/community/PaddlePaddle/ernie_vil-2.0-base-zh/vocab.txt", "1c1c1f4fd93c5bed3b4eebec4de976a8", ], "preprocessor_config": [ "https://paddlenlp.bj.bcebos.com/models/community/PaddlePaddle/ernie_vil-2.0-base-zh/preprocessor_config.json", "9a2e8da9f41896fedb86756b79355ee2", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/models/community/PaddlePaddle/ernie_vil-2.0-base-zh/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/models/community/PaddlePaddle/ernie_vil-2.0-base-zh/tokenizer_config.json", "da5385c23c8f522d33fc3aac829e4375", ], }, "OFA-Sys/chinese-clip-vit-base-patch16": { "model_state": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-base-patch16/model_state.pdparams", "d594c94833b8cfeffc4f986712b3ef79", ], "config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-base-patch16/config.json", "3611b5c34ad69dcf91e3c1d03b01a93a", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-base-patch16/vocab.txt", "3b5b76c4aef48ecf8cb3abaafe960f09", ], "preprocessor_config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-base-patch16/preprocessor_config.json", "ba1fb66c75b18b3c9580ea5120e01ced", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-base-patch16/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-base-patch16/tokenizer_config.json", "573ba0466e15cdb5bd423ff7010735ce", ], }, "OFA-Sys/chinese-clip-vit-large-patch14": { "model_state": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14/model_state.pdparams", "5c0dde02d68179a9cc566173e53966c0", ], "config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14/config.json", "a5e35843aa87ab1106e9f60f1e16b96d", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14/vocab.txt", "3b5b76c4aef48ecf8cb3abaafe960f09", ], "preprocessor_config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14/preprocessor_config.json", "ba1fb66c75b18b3c9580ea5120e01ced", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14/tokenizer_config.json", "573ba0466e15cdb5bd423ff7010735ce", ], }, "OFA-Sys/chinese-clip-vit-large-patch14-336px": { "model_state": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14-336px/model_state.pdparams", "ee3eb7f9667cfb06338bea5757c5e0d7", ], "config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14-336px/config.json", "cb2794d99bea8c8f45901d177e663e1e", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14-336px/vocab.txt", "3b5b76c4aef48ecf8cb3abaafe960f09", ], "preprocessor_config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14-336px/preprocessor_config.json", "c52a0b3abe9bdd1c3c5a3d56797f4a03", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14-336px/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/models/community/OFA-Sys/chinese-clip-vit-large-patch14-336px/tokenizer_config.json", "573ba0466e15cdb5bd423ff7010735ce", ], }, "__internal_testing__/tiny-random-ernievil2": { "model_state": [ "https://paddlenlp.bj.bcebos.com/models/community/__internal_testing__/tiny-random-ernievil2/model_state.pdparams", "771c844e7b75f61123d9606c8c17b1d6", ], "config": [ "https://paddlenlp.bj.bcebos.com/models/community/__internal_testing__/tiny-random-ernievil2/config.json", "ae27a68336ccec6d3ffd14b48a6d1f25", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/models/community/__internal_testing__/tiny-random-ernievil2/vocab.txt", "1c1c1f4fd93c5bed3b4eebec4de976a8", ], "preprocessor_config": [ "https://paddlenlp.bj.bcebos.com/models/community/__internal_testing__/tiny-random-ernievil2/preprocessor_config.json", "9a2e8da9f41896fedb86756b79355ee2", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/models/community/__internal_testing__/tiny-random-ernievil2/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/models/community/__internal_testing__/tiny-random-ernievil2/tokenizer_config.json", "2333f189cad8dd559de61bbff4d4a789", ], }, } def __init__(self, task, model, batch_size=1, is_static_model=True, max_length=128, return_tensors="pd", **kwargs): super().__init__(task=task, model=model, **kwargs) self._seed = None self.export_type = "text" self._batch_size = batch_size self.return_tensors = return_tensors if not self.from_hf_hub: self._check_task_files() self._max_length = max_length self._construct_tokenizer() self.is_static_model = is_static_model self._config_map = {} self.predictor_map = {} self.input_names_map = {} self.input_handles_map = {} self.output_handle_map = {} self._check_predictor_type() if self.is_static_model: self._get_inference_model() else: self._construct_model(model) def _construct_model(self, model): """ Construct the inference model for the predictor. """ self._model = AutoModel.from_pretrained(self._task_path) self._model.eval() def _construct_tokenizer(self): """ Construct the tokenizer for the predictor. """ self._processor = AutoProcessor.from_pretrained(self._task_path) def _batchify(self, data, batch_size): """ Generate input batches. """ def _parse_batch(batch_examples): if isinstance(batch_examples[0], str): batch_texts = batch_examples batch_images = None else: batch_texts = None batch_images = batch_examples if self.is_static_model: # The input of static model is numpy array tokenized_inputs = self._processor( text=batch_texts, images=batch_images, return_tensors="np", padding="max_length", max_length=self._max_length, truncation=True, ) else: # The input of dygraph model is padddle.Tensor tokenized_inputs = self._processor( text=batch_texts, images=batch_images, return_tensors="pd", padding="max_length", max_length=self._max_length, truncation=True, ) return tokenized_inputs # Seperates data into some batches. one_batch = [] for example in data: one_batch.append(example) if len(one_batch) == batch_size: yield _parse_batch(one_batch) one_batch = [] if one_batch: yield _parse_batch(one_batch) def _check_input_text(self, inputs): """ Check whether the input text meet the requirement. """ inputs = inputs[0] if isinstance(inputs, str): if len(inputs) == 0: raise ValueError("Invalid inputs, input text should not be empty, please check your input.") inputs = [inputs] elif isinstance(inputs, Image.Image): inputs = [inputs] elif isinstance(inputs, list): # and len(inputs[0].strip()) > 0 if not (isinstance(inputs[0], (str, Image.Image))): raise TypeError( "Invalid inputs, input text/image should be list of str/PIL.image, and first element of list should not be empty." ) else: raise TypeError( "Invalid inputs, input text should be str or list of str, but type of {} found!".format(type(inputs)) ) return inputs def _preprocess(self, inputs): """ Transform the raw inputs to the model inputs, two steps involved: 1) Transform the raw text/image to token ids/pixel_values. 2) Generate the other model inputs from the raw text/image and token ids/pixel_values. """ inputs = self._check_input_text(inputs) batches = self._batchify(inputs, self._batch_size) outputs = {"batches": batches, "inputs": inputs} return outputs def _run_model(self, inputs): """ Run the task model from the outputs of the `_preprocess` function. """ all_feats = [] if self.is_static_model: with static_mode_guard(): for batch_inputs in inputs["batches"]: if self._predictor_type == "paddle-inference": if "input_ids" in batch_inputs: self.input_handles_map["text"][0].copy_from_cpu(batch_inputs["input_ids"]) self.predictor_map["text"].run() text_features = self.output_handle_map["text"][0].copy_to_cpu() all_feats.append(text_features) elif "pixel_values" in batch_inputs: self.input_handles_map["image"][0].copy_from_cpu(batch_inputs["pixel_values"]) self.predictor_map["image"].run() image_features = self.output_handle_map["image"][0].copy_to_cpu() all_feats.append(image_features) else: # onnx mode if "input_ids" in batch_inputs: input_dict = {} input_dict["input_ids"] = batch_inputs["input_ids"] text_features = self.predictor_map["text"].run(None, input_dict)[0].tolist() all_feats.append(text_features) elif "pixel_values" in batch_inputs: input_dict = {} input_dict["pixel_values"] = batch_inputs["pixel_values"] image_features = self.predictor_map["image"].run(None, input_dict)[0].tolist() all_feats.append(image_features) else: for batch_inputs in inputs["batches"]: if "input_ids" in batch_inputs: text_features = self._model.get_text_features(input_ids=batch_inputs["input_ids"]) all_feats.append(text_features.numpy()) if "pixel_values" in batch_inputs: image_features = self._model.get_image_features(pixel_values=batch_inputs["pixel_values"]) all_feats.append(image_features.numpy()) inputs.update({"features": all_feats}) return inputs def _postprocess(self, inputs): inputs["features"] = np.concatenate(inputs["features"], axis=0) if self.return_tensors == "pd": inputs["features"] = paddle.to_tensor(inputs["features"]) return inputs def _construct_input_spec(self): """ Construct the input spec for the convert dygraph model to static model. """ self._input_text_spec = [ paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"), ] self._input_image_spec = [ paddle.static.InputSpec(shape=[None, 3, 224, 224], dtype="float32", name="pixel_values"), ] def _convert_dygraph_to_static(self): """ Convert the dygraph model to static model. """ assert ( self._model is not None ), "The dygraph model must be created before converting the dygraph model to static model." assert ( self._input_image_spec is not None or self._input_text_spec is not None ), "The input spec must be created before converting the dygraph model to static model." logger.info("Converting to the inference model cost a little time.") static_model = paddle.jit.to_static(self._model.get_text_features, input_spec=self._input_text_spec) self.inference_model_path = self.inference_text_model_path paddle.jit.save(static_model, self.inference_model_path) logger.info("The inference model save in the path:{}".format(self.inference_model_path)) static_model = paddle.jit.to_static(self._model.get_image_features, input_spec=self._input_image_spec) self.inference_model_path = self.inference_image_model_path paddle.jit.save(static_model, self.inference_model_path) logger.info("The inference model save in the path:{}".format(self.inference_model_path)) def _get_inference_model(self): """ Return the inference program, inputs and outputs in static mode. """ _base_path = os.path.join(self._home_path, "taskflow", self.task, self.model) self.inference_image_model_path = os.path.join(_base_path, "static", "get_image_features") self.inference_text_model_path = os.path.join(_base_path, "static", "get_text_features") if ( not os.path.exists(self.inference_image_model_path + ".pdiparams") or self._param_updated or not os.path.exists(self.inference_text_model_path + ".pdiparams") ): with dygraph_mode_guard(): self._construct_model(self.model) self._construct_input_spec() self._convert_dygraph_to_static() if self._predictor_type == "paddle-inference": # Get text inference model self.inference_model_path = self.inference_text_model_path self._static_model_file = self.inference_model_path + ".pdmodel" self._static_params_file = self.inference_model_path + ".pdiparams" self._config = paddle.inference.Config(self._static_model_file, self._static_params_file) self._prepare_static_mode() self.predictor_map["text"] = self.predictor self.input_names_map["text"] = self.input_names self.input_handles_map["text"] = self.input_handles self.output_handle_map["text"] = self.output_handle self._config_map["text"] = self._config # Get image inference model self.inference_model_path = self.inference_image_model_path self._static_model_file = self.inference_model_path + ".pdmodel" self._static_params_file = self.inference_model_path + ".pdiparams" self._config = paddle.inference.Config(self._static_model_file, self._static_params_file) self._prepare_static_mode() self.predictor_map["image"] = self.predictor self.input_names_map["image"] = self.input_names self.input_handles_map["image"] = self.input_handles self.output_handle_map["image"] = self.output_handle self._config_map["image"] = self._config else: # Get text onnx model self.export_type = "text" self.inference_model_path = self.inference_text_model_path self._static_model_file = self.inference_model_path + ".pdmodel" self._static_params_file = self.inference_model_path + ".pdiparams" self._prepare_onnx_mode() self.predictor_map["text"] = self.predictor # Get image onnx model self.export_type = "image" self.inference_model_path = self.inference_image_model_path self._static_model_file = self.inference_model_path + ".pdmodel" self._static_params_file = self.inference_model_path + ".pdiparams" self._prepare_onnx_mode() self.predictor_map["image"] = self.predictor