import os import h5py import json import pickle import argparse import torch import numpy as np from PIL import Image from tqdm import tqdm import torchvision.transforms as transforms from model.densecap import densecap_resnet50_fpn model = None first_run = True def load_model(console_args): with open(console_args.config_json, 'r') as f: model_args = json.load(f) model = densecap_resnet50_fpn(backbone_pretrained=model_args['backbone_pretrained'], return_features=console_args.extract, feat_size=model_args['feat_size'], hidden_size=model_args['hidden_size'], max_len=model_args['max_len'], emb_size=model_args['emb_size'], rnn_num_layers=model_args['rnn_num_layers'], vocab_size=model_args['vocab_size'], fusion_type=model_args['fusion_type'], box_detections_per_img=console_args.box_per_img) checkpoint = torch.load(console_args.model_checkpoint, map_location=torch.device('cpu')) model.load_state_dict(checkpoint['model']) if console_args.verbose and 'results_on_val' in checkpoint.keys(): print('[INFO]: checkpoint {} loaded'.format(console_args.model_checkpoint)) print('[INFO]: correspond performance on val set:') for k, v in checkpoint['results_on_val'].items(): if not isinstance(v, dict): print(' {}: {:.3f}'.format(k, v)) return model def get_image_path(console_args): img_list = [] if os.path.isdir(console_args.img_path): for file_name in os.listdir(console_args.img_path): img_list.append(os.path.join(console_args.img_path, file_name)) else: img_list.append(console_args.img_path) return img_list def img_to_tensor(img_list): assert isinstance(img_list, list) and len(img_list) > 0 img_tensors = [] for img_path in img_list: img = Image.open(img_path).convert("RGB") img_tensors.append(transforms.ToTensor()(img)) return img_tensors def describe_images(model, img_list, device, console_args): assert isinstance(img_list, list) assert isinstance(console_args.batch_size, int) and console_args.batch_size > 0 all_results = [] with torch.no_grad(): model.to(device) model.eval() for i in tqdm(range(0, len(img_list), console_args.batch_size), disable=not console_args.verbose): image_tensors = img_to_tensor(img_list[i:i+console_args.batch_size]) input_ = [t.to(device) for t in image_tensors] results = model(input_) all_results.extend([{k:v.cpu() for k,v in r.items()} for r in results]) return all_results def save_results_to_file(img_list, all_results, console_args): with open(os.path.join(console_args.lut_path), 'rb') as f: look_up_tables = pickle.load(f) idx_to_token = look_up_tables['idx_to_token'] results_dict = {} if console_args.extract: total_box = sum(len(r['boxes']) for r in all_results) start_idx = 0 img_idx = 0 h = h5py.File(os.path.join(console_args.result_dir, 'box_feats.h5'), 'w') h.create_dataset('feats', (total_box, all_results[0]['feats'].shape[1]), dtype=np.float32) h.create_dataset('boxes', (total_box, 4), dtype=np.float32) h.create_dataset('start_idx', (len(img_list),), dtype=np.long) h.create_dataset('end_idx', (len(img_list),), dtype=np.long) for img_path, results in zip(img_list, all_results): if console_args.verbose: print('[Result] ==== {} ====='.format(img_path)) results_dict[img_path] = [] for box, cap, score in zip(results['boxes'], results['caps'], results['scores']): r = { 'box': [round(c, 2) for c in box.tolist()], 'score': round(score.item(), 2), 'cap': ' '.join(idx_to_token[idx] for idx in cap.tolist() if idx_to_token[idx] not in ['', '', '']) } if console_args.verbose and r['score'] > 0.9: print(' SCORE {} BOX {}'.format(r['score'], r['box'])) print(' CAP {}\n'.format(r['cap'])) results_dict[img_path].append(r) if console_args.extract: box_num = len(results['boxes']) h['feats'][start_idx: start_idx+box_num] = results['feats'].cpu().numpy() h['boxes'][start_idx: start_idx+box_num] = results['boxes'].cpu().numpy() h['start_idx'][img_idx] = start_idx h['end_idx'][img_idx] = start_idx + box_num - 1 start_idx += box_num img_idx += 1 if console_args.extract: h.close() # save order of img to a txt if len(img_list) > 1: with open(os.path.join(console_args.result_dir, 'feat_img_mappings.txt'), 'w') as f: for img_path in img_list: f.writelines(os.path.split(img_path)[1] + '\n') if not os.path.exists(console_args.result_dir): os.mkdir(console_args.result_dir) with open(os.path.join(console_args.result_dir, 'result.json'), 'w') as f: json.dump(results_dict, f, indent=2) if console_args.verbose: print('[INFO] result save to {}'.format(os.path.join(console_args.result_dir, 'result.json'))) if console_args.extract: print('[INFO] feats save to {}'.format(os.path.join(console_args.result_dir, 'box_feats.h5'))) print('[INFO] order save to {}'.format(os.path.join(console_args.result_dir, 'feat_img_mappings.txt'))) def validate_box_feat(model, all_results, device, console_args): with torch.no_grad(): box_describer = model.roi_heads.box_describer box_describer.to(device) box_describer.eval() if console_args.verbose: print('[INFO] start validating box features...') for results in tqdm(all_results, disable=not console_args.verbose): captions = box_describer(results['feats'].to(device)) assert (captions.cpu() == results['caps']).all().item(), 'caption mismatch' if console_args.verbose: print('[INFO] validate box feat done, no problem') def main(console_args): global model global first_run device = torch.device("cuda" if torch.cuda.is_available() and not args.cpu else "cpu") # === prepare images ==== img_list = get_image_path(console_args) # === prepare model ==== if first_run: model = load_model(console_args) first_run = False # === inference ==== all_results = describe_images(model, img_list, device, console_args) # === save results ==== save_results_to_file(img_list, all_results, console_args) if console_args.extract and console_args.check: validate_box_feat(model, all_results, device, console_args) def process_image(image, folder_location): global args parser = argparse.ArgumentParser(description='Do dense captioning') parser.add_argument('--config_json', type=str, help="path of the json file which stored model configuration") parser.add_argument('--lut_path', type=str, default=f'{folder_location}CircumSpect/data/VG-regions-dicts-lite.pkl', help='look up table path') parser.add_argument('--model_checkpoint', type=str, help="path of the trained model checkpoint") parser.add_argument('--img_path', type=str, help="path of images, should be a file or a directory with only images") parser.add_argument('--result_dir', type=str, default='.', help="path of the directory to save the output file") parser.add_argument('--box_per_img', type=int, default=100, help='max boxes to describe per image') parser.add_argument('--batch_size', type=int, default=1, help="useful when img_path is a directory") parser.add_argument('--extract', action='store_true', help='whether to extract features') parser.add_argument('--cpu', action='store_true', help='whether use cpu to compute') parser.add_argument('--verbose', action='store_true', help='whether output info') parser.add_argument('--check', action='store_true', help='whether to validate box feat by regenerate sentences') args = argparse.Namespace() args.config_json = f'{folder_location}/model_params/train_all_val_all_bz_2_epoch_10_inject_init/config.json' args.lut_path = f'{folder_location}/data/VG-regions-dicts-lite.pkl' args.model_checkpoint = f'{folder_location}model_params/train_all_val_all_bz_2_epoch_10_inject_init.pth.tar' args.img_path = image args.result_dir = f'{folder_location}' args.box_per_img = 100 args.batch_size = 2 args.extract = False args.cpu = False args.verbose = True args.check = False main(args)