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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 ['<pad>', '<bos>', '<eos>'])
}
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)
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