expansionnet-v2/expansionnet_v2.py

# 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
import os
import pathlib
import pickle
from argparse import Namespace

import torch
import torchvision 
from torchvision import transforms
from transformers import GPT2Tokenizer

from towhee.types.arg import arg, to_image_color
from towhee.types.image_utils import to_pil
from towhee.operator.base import NNOperator, OperatorFlag
from towhee import register
from towhee.models import clip
from towhee.command.s3 import S3Bucket

class ExpansionNetV2(NNOperator):
    """
    ExpansionNet V2 image captioning operator
    """
    def __init__(self, model_name: str):
        super().__init__()
        path = str(pathlib.Path(__file__).parent)
        sys.path.append(path)
        from models.End_ExpansionNet_v2 import End_ExpansionNet_v2
        from utils.language_utils import convert_vector_idx2word
        self.convert_vector_idx2word = convert_vector_idx2word

        cfg = self._configs()[model_name]

        s3_bucket = S3Bucket()
        s3_bucket.download_file(cfg['weights'], path + '/weights/')

        sys.path.pop()
        with open('{}/demo_coco_tokens.pickle'.format(path), 'rb') as f:
            coco_tokens = pickle.load(f)
            self.coco_tokens = coco_tokens

        img_size = 384
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        drop_args = Namespace(enc=0.0,
                      dec=0.0,
                      enc_input=0.0,
                      dec_input=0.0,
                      other=0.0)

        drop_args = Namespace(enc=0.0,
                              dec=0.0,
                              enc_input=0.0,
                              dec_input=0.0,
                              other=0.0)
        model_args = Namespace(model_dim=512,
                               N_enc=3,
                               N_dec=3,
                               dropout=0.0,
                               drop_args=drop_args)
        max_seq_len = 74
        beam_size = 5
        self.model = End_ExpansionNet_v2(swin_img_size=img_size, swin_patch_size=4, swin_in_chans=3,
                                    swin_embed_dim=192, swin_depths=[2, 2, 18, 2], swin_num_heads=[6, 12, 24, 48],
                                    swin_window_size=12, swin_mlp_ratio=4., swin_qkv_bias=True, swin_qk_scale=None,
                                    swin_drop_rate=0.0, swin_attn_drop_rate=0.0, swin_drop_path_rate=0.0,
                                    swin_norm_layer=torch.nn.LayerNorm, swin_ape=False, swin_patch_norm=True,
                                    swin_use_checkpoint=False,
                                    final_swin_dim=1536,

                                    d_model=model_args.model_dim, N_enc=model_args.N_enc,
                                    N_dec=model_args.N_dec, num_heads=8, ff=2048,
                                    num_exp_enc_list=[32, 64, 128, 256, 512],
                                    num_exp_dec=16,
                                    output_word2idx=coco_tokens['word2idx_dict'],
                                    output_idx2word=coco_tokens['idx2word_list'],
                                    max_seq_len=max_seq_len, drop_args=model_args.drop_args,
                                rank='cpu')

        checkpoint = torch.load('{}/weights/{}'.format(path,os.path.basename(cfg['weights'])), map_location=torch.device('cpu'))
        self.model.load_state_dict(checkpoint['model_state_dict'])
        self.model.to(self.device)

        self.transf_1 = torchvision.transforms.Compose([torchvision.transforms.Resize((img_size, img_size)), torchvision.transforms.ToTensor()])
        self.transf_2 = torchvision.transforms.Compose([torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
        self.beam_search_kwargs = {'beam_size': beam_size,
                                   'beam_max_seq_len': max_seq_len,
                                   'sample_or_max': 'max',
                                   'how_many_outputs': 1,
                                   'sos_idx': coco_tokens['word2idx_dict'][coco_tokens['sos_str']],
                                   'eos_idx': coco_tokens['word2idx_dict'][coco_tokens['eos_str']]}
    def _preprocess(self, img):
        img = to_pil(img)
        processed_img = self.transf_1(img)
        processed_img = self.transf_2(processed_img)
        processed_img = processed_img.to(self.device)
        return processed_img

    @arg(1, to_image_color('RGB'))
    def inference_single_data(self, data):
        text = self._inference_from_image(data)
        return text

    def __call__(self, data):
        if not isinstance(data, list):
            data = [data]
        else:
            data = data
        results = []
        for single_data in data:
            result = self.inference_single_data(single_data)
            results.append(result)
        if len(data) == 1:
            return results[0]
        else:
            return results

    @arg(1, to_image_color('RGB'))
    def _inference_from_image(self, img):
        img = self._preprocess(img).unsqueeze(0)
        with torch.no_grad():
            pred, _ = self.model(enc_x=img,
                            enc_x_num_pads=[0],
                            mode='beam_search', **self.beam_search_kwargs)
        pred = self.convert_vector_idx2word(pred[0][0], self.coco_tokens['idx2word_list'])[1:-1]
        pred[-1] = pred[-1] + '.'
        pred = ' '.join(pred).capitalize()

        return pred

    def _configs(self):
        config = {}
        config['expansionnet_rf'] = {}
        config['expansionnet_rf']['weights'] = 'image-captioning/expansionnet-v2/rf_model.pth'
        return config