# Copyright 2021 Zilliz. 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 sys from pathlib import Path import torch from torch import nn from transformers import AutoProcessor, BlipForImageTextRetrieval from transformers import logging as t_logging from towhee import register from towhee.operator.base import NNOperator, OperatorFlag from towhee.types.arg import arg, to_image_color log = logging.getLogger('run_op') warnings.filterwarnings('ignore') os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' t_logging.set_verbosity_error() #@accelerate class BLIPModelVision(nn.Module): def __init__(self, model): super().__init__() self.model = model def forward(self, image): image_embeds = self.model.visual_encoder(image) image_embeds = self.model.vision_proj(image_embeds[:,0,:]) return image_embeds #@accelerate class BLIPModelText(nn.Module): def __init__(self, model): super().__init__() self.model = model def forward(self, input_ids, attention_mask): text_features = self.model.text_encoder(input_ids, attention_mask = attention_mask, return_dict = False)[0] text_features = self.model.text_proj(text_features[:,0,:]) return text_features @register(output_schema=['vec']) class Blip(NNOperator): """ BLIP multi-modal embedding operator """ def __init__(self, model_name: str, modality: str, device:str = 'cpu', checkpoint_path: str = None): super().__init__() self.modality = modality self.model_name = model_name self.device = device cfg = self._configs()[model_name] try: blip_model = BlipForImageTextRetrieval.from_pretrained(cfg) except Exception as e: log.error(f'Fail to load model by name: {self.model_name}') raise e if checkpoint_path: try: state_dict = torch.load(checkpoint_path, map_location=self.device) self.model.load_state_dict(state_dict) except Exception as e: log.error(f'Fail to load state dict from {checkpoint_path}: {e}') self.processor = AutoProcessor.from_pretrained('Salesforce/blip-itm-base-coco') if self.modality == 'image': self.model = BLIPModelVision(blip_model) elif self.modality == 'text': self.model = BLIPModelText(blip_model) else: raise ValueError('modality[{}] not implemented.'.format(self.modality)) self._modality = modality self.device = 'cuda' if torch.cuda.is_available() else 'cpu' self.model.to(self.device) self.model.eval() def __call__(self, data): if self._modality == 'image': vec = self._inference_from_image(data) elif self._modality == 'text': vec = self._inference_from_text(data) else: raise ValueError('modality[{}] not implemented.'.format(self._modality)) return vec.detach().cpu().numpy().flatten() def _inference_from_text(self, text): inputs = self.processor(text=text, padding=True, return_tensors='pt') inputs = inputs.to(self.device) text_feature = self.model(input_ids = inputs.input_ids, attention_mask = inputs.attention_mask)[0] return text_feature @arg(1, to_image_color('RGB')) def _inference_from_image(self, img): inputs = self.processor(images=img, return_tensors='pt') inputs = inputs.to(self.device) image_feature = self.model(inputs) return image_feature def _configs(self): config = {} config['blip_itm_base'] = {} config['blip_itm_base']['weights'] = 'Salesforce/blip-itm-base-coco' config['blip_itm_base']['image_size'] = 224 return config @property def _model(self): return self.model def train(self, **kwargs): raise NotImplementedError @property def supported_formats(self): onnxes = self.supported_model_names(format='onnx') if self.model_name in onnxes: return ['onnx'] else: return ['pytorch'] @staticmethod def supported_model_names(format: str = None): if format == 'pytorch' or format == 'torchscript' or format == 'onnx': model_list = [ 'blip_itm_base', ] else: log.error(f'Invalid format "{format}". Currently supported formats: "pytorch", "torchscript".') return model_list def save_model(self, model_type: str = 'pytorch', output_file: str = 'default'): import os from PIL import Image from torch.onnx import export as onnx_export if output_file == 'default': output_file = str(Path(__file__).parent) output_file = os.path.join(output_file, 'saved', model_type) os.makedirs(output_file, exist_ok=True) name = self.model_name.replace('/', '-') output_file = os.path.join(output_file, name) if model_type in ['pytorch', 'torchscript']: output_file = output_file + '.pt' elif model_type == 'onnx': output_file = output_file + '.onnx' else: raise AttributeError('Unsupported model_type.') if self.modality == 'image': sz = self.processor.feature_extractor.crop_size if isinstance(sz, int): h = sz w = sz elif isinstance(sz, dict): h = sz['height'] w = sz['width'] dummy_input = Image.new('RGB', (w, h), color = 'red') inputs = self.processor(images=dummy_input, return_tensors='pt') # a dictionary elif self.modality == 'text': dummy_input = 'dummy' inputs = self.processor(text=dummy_input, padding=True, return_tensors='pt') else: raise ValueError('modality[{}] not implemented.'.format(self.modality)) if model_type == 'pytorch': torch.save(self._model, output_file) elif model_type == 'torchscript': inputs = list(inputs.values()) try: try: jit_model = torch.jit.script(self._model) except Exception: jit_model = torch.jit.trace(self._model, inputs, strict=False) torch.jit.save(jit_model, output_file) except Exception as e: log.error(f'Fail to save as torchscript: {e}.') raise RuntimeError(f'Fail to save as torchscript: {e}.') elif model_type == 'onnx': if self.modality == 'image': input_names= ['pixel_values'] output_names=['image_embeds'] dynamic_axes={'pixel_values': {0: 'batch'}, 'image_embeds': {0: 'batch'}} elif self.modality == 'text': input_names= ['input_ids', 'attention_mask'] output_names=['text_embeds'] dynamic_axes={'input_ids': {0: 'batch', 1: 'sequence'}, 'attention_mask': {0: 'batch', 1: 'sequence'}, 'text_embeds': {0: 'batch'}} else: raise ValueError('modality[{}] not implemented.'.format(self.modality)) onnx_export(self.model, (dict(inputs),), f=Path(output_file), input_names= input_names, output_names=output_names, dynamic_axes=dynamic_axes, do_constant_folding=True, opset_version=14, ) else: pass raise NotImplementedError