Add Image Text Retrieval taskflow&pipelines API #4516
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| # 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 | ||
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| import numpy as np | ||
| import paddle | ||
| from PIL import Image | ||
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| from ..transformers import AutoModel, AutoProcessor | ||
| from ..utils.log import logger | ||
| from .task import Task | ||
| from .utils import dygraph_mode_guard, static_mode_guard | ||
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| class MultimodalFeatureExtractionTask(Task): | ||
| """ | ||
| The text_to_image generation model to generate the image. | ||
| 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. | ||
| """ | ||
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| def __init__(self, task, model, batch_size=1, _static_mode=True, **kwargs): | ||
| super().__init__(task=task, model=model, **kwargs) | ||
| self._seed = None | ||
| # we do not use batch | ||
| self._batch_size = batch_size | ||
| self._construct_tokenizer(model_name=model) | ||
| self._static_mode = _static_mode | ||
| self._config_map = {} | ||
| self.predictor_map = {} | ||
| self.input_names_map = {} | ||
| self.input_handles_map = {} | ||
| self.output_handle_map = {} | ||
| if self._static_mode: | ||
| self._get_inference_model() | ||
| else: | ||
| self._construct_model(model) | ||
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| def _construct_model(self, model): | ||
| """ | ||
| Construct the inference model for the predictor. | ||
| """ | ||
| self._model = AutoModel.from_pretrained(model) | ||
| self._model.eval() | ||
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| def _construct_tokenizer(self, model_name): | ||
| """ | ||
| Construct the tokenizer for the predictor. | ||
| """ | ||
| self._processor = AutoProcessor.from_pretrained(model_name) | ||
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| def _batchify(self, data, batch_size): | ||
| """ | ||
| Generate input batches. | ||
| """ | ||
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| 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._static_mode: | ||
| tokenized_inputs = self._processor( | ||
| text=batch_texts, images=batch_images, return_tensors="np", padding="max_length", truncation=True | ||
| ) | ||
| else: | ||
| tokenized_inputs = self._processor( | ||
| text=batch_texts, images=batch_images, return_tensors="pd", padding="max_length", truncation=True | ||
| ) | ||
| return tokenized_inputs | ||
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| # 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) | ||
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| def _check_input_text(self, inputs): | ||
| """ | ||
| Check whether the input text meet the requirement. | ||
| """ | ||
| inputs = inputs[0] | ||
| if isinstance(inputs, (str, Image.Image)): | ||
| if len(inputs) == 0: | ||
| raise ValueError("Invalid inputs, input text/image should not be empty, please check your input.") | ||
| 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 | ||
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| def _preprocess(self, inputs): | ||
| """ | ||
| Transform the raw text to the model inputs, two steps involved: | ||
| 1) Transform the raw text to token ids. | ||
| 2) Generate the other model inputs from the raw text and token ids. | ||
| """ | ||
| inputs = self._check_input_text(inputs) | ||
| batches = self._batchify(inputs, self._batch_size) | ||
| outputs = {"batches": batches, "text": inputs} | ||
| return outputs | ||
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| def _run_model(self, inputs): | ||
| """ | ||
| Run the task model from the outputs of the `_preprocess` function. | ||
| """ | ||
| all_feats = [] | ||
| if self._static_mode: | ||
| with static_mode_guard(): | ||
| for batch_inputs in inputs["batches"]: | ||
| 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: | ||
| 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) | ||
| if "pixel_values" in batch_inputs: | ||
| image_features = self._model.get_image_features(pixel_values=batch_inputs["pixel_values"]) | ||
| all_feats.append(image_features) | ||
| inputs.update({"features": all_feats}) | ||
| return inputs | ||
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| def _postprocess(self, inputs): | ||
| if self._static_mode: | ||
| inputs["features"] = paddle.to_tensor(np.concatenate(inputs["features"], axis=0)) | ||
| else: | ||
| inputs["features"] = paddle.concat(inputs["features"], axis=0) | ||
| return inputs | ||
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| def _construct_input_spec(self): | ||
| """ | ||
| Construct the input spec for the convert dygraph model to static model. | ||
| """ | ||
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| self._input_text_spec = [ | ||
| paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"), | ||
| ] | ||
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| self._input_image_spec = [ | ||
| paddle.static.InputSpec(shape=[None, 3, 224, 224], dtype="float32", name="pixel_values"), | ||
| ] | ||
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| 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.") | ||
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| 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)) | ||
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| 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)) | ||
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| 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() | ||
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| 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 | ||
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| # 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() | ||
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| 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: | ||
| self._prepare_onnx_mode() | ||
|
There was a problem hiding this comment. 如果输入和输出都已经改了，onnx mode看起来也是需要修改，同时需要FP16 mode |
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这个key叫batchs有点歧义，features之类的？
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已沟通，保持不变