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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import json
import math
import os
import os.path as osp
import re
import torch
import tqdm
from mmengine.dist import (collect_results, get_dist_info, get_rank, init_dist,
master_only)
from mmengine.utils.dl_utils import set_multi_processing
from torch.utils.data import Dataset
from transformers import (AutoModel, AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig, CLIPImageProcessor,
CLIPVisionModel, GenerationConfig)
from projects.omg_llava.model.utils import prepare_inputs_labels_for_multimodal_with_visual_prompts
from xtuner.tools.utils import get_stop_criteria, is_cn_string
from xtuner.utils import (DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX,
PROMPT_TEMPLATE)
from xtuner.registry import BUILDER
from xtuner.configs import cfgs_name_path
from xtuner.model.utils import guess_load_checkpoint
from mmengine.config import Config
from mmengine.fileio import PetrelBackend, get_file_backend
from mmengine.config import ConfigDict
from PIL import Image
import torch.nn.functional as F
from projects.omg_llava.dataset.utils import expand2square, expand2square_mask
from pycocotools import mask
from pycocotools.coco import COCO
import numpy as np
def bbox_to_x1y1x2y2(bbox):
x1, y1, w, h = bbox
bbox = [x1, y1, x1 + w, y1 + h]
return bbox
def convert_dict2config_dict(input):
input = ConfigDict(**input)
for key in input.keys():
if isinstance(input[key], dict):
input[key] = convert_dict2config_dict(input[key])
return input
TORCH_DTYPE_MAP = dict(
fp16=torch.float16, bf16=torch.bfloat16, fp32=torch.float32, auto='auto')
def parse_args():
parser = argparse.ArgumentParser(description='RefCocoSeg')
parser.add_argument('config', help='config file name or path.')
parser.add_argument('--pth_model', help='pth model file')
parser.add_argument(
'--output-path', type=str, default='./1215_demos/object_cap_sa2va.json', help='Name for Bot')
parser.add_argument(
'--prompt-template',
choices=PROMPT_TEMPLATE.keys(),
default='internlm2_chat',
help='Specify a prompt template')
parser.add_argument(
'--stop-words', nargs='+', type=str, default=[], help='Stop words')
parser.add_argument(
'--torch-dtype',
default='fp16',
choices=TORCH_DTYPE_MAP.keys(),
help='Override the default `torch.dtype` and load the model under '
'a specific `dtype`.')
parser.add_argument(
'--bits',
type=int,
choices=[4, 8, None],
default=None,
help='LLM bits')
parser.add_argument(
'--bot-name', type=str, default='BOT', help='Name for Bot')
parser.add_argument(
'--offload-folder',
default=None,
help='The folder in which to offload the model weights (or where the '
'model weights are already offloaded).')
parser.add_argument(
'--max-new-tokens',
type=int,
default=300,
help='Maximum number of new tokens allowed in generated text')
parser.add_argument(
'--seed',
type=int,
default=0,
help='Random seed for reproducible text generation')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
args = parser.parse_args()
return args
@master_only
def master_print(msg):
print(msg)
class RegionCap_Inference_Dataset(Dataset):
def __init__(self,
image_folder,
metainfo=None,
):
self.metainfo = metainfo
self.image_folder = image_folder
image_files = []
for file_name in os.listdir(self.image_folder):
if 'out' not in file_name and '.jpg' in file_name:
image_files.append(file_name)
json_files = []
for file_name in image_files:
json_files.append(file_name.replace('.jpg', '_out.json'))
self.image_files = image_files
self.json_files = json_files
self.data_dicts = []
for image_file, json_file in zip(image_files, json_files):
with open(os.path.join(image_folder, json_file), 'r') as f:
_datas = json.load(f)
for _data in _datas:
self.data_dicts.append({'image_file': image_file, 'object_anno': _data})
def __len__(self):
return len(self.data_dicts)
def decode_mask(self, rle):
m = mask.decode(rle)[None]
print(m.shape)
return m
def get_questions(self):
# question = "<image>Can you provide me with a detailed description of the region in the picture marked by region1."
question = "<image>Please give me a short description of the region in the picture marked by region1."
return question
def __getitem__(self, index):
_json_info = self.data_dicts[index]
data_dict = {}
image_id = index
image_file = _json_info['image_file']
questions = self.get_questions()
data_dict['image_file'] = image_file
image_file = os.path.join(self.image_folder, image_file)
image = Image.open(image_file).convert('RGB')
masks = _json_info['object_anno']['segmentation']
masks = self.decode_mask(masks)
data_dict['pixel_values'] = image
data_dict['ori_image'] = image
data_dict['text_prompts'] = questions
ori_width, ori_height = image.size
data_dict['ori_image_size'] = (ori_width, ori_height)
data_dict['img_id'] = image_id
data_dict['vp'] = True
data_dict['mask_prompts'] = [masks]
mask_image = self.get_mask_image(image, masks[0])
mask_image.save(os.path.join('./1215_demos/object_demos/', f"{image_id}.png"))
return data_dict
def get_mask_image(self, image, mask):
image_shape = image.size
mask = torch.Tensor(mask).unsqueeze(0).unsqueeze(0)
mask = F.interpolate(
mask,
size=(image_shape[1], image_shape[0]),
mode='nearest').squeeze(0).squeeze(0)
mask = mask.numpy()
image = copy.deepcopy(image)
image = np.array(image)
image = image * 0.5
image[:, :, 0] = image[:, :, 0] + mask * 255 * 0.5
image = np.clip(image, 0, 255).astype(np.uint8)
return Image.fromarray(image)
def main():
args = parse_args()
torch.manual_seed(args.seed)
if args.launcher != 'none':
set_multi_processing(distributed=True)
init_dist(args.launcher)
rank, world_size = get_dist_info()
torch.cuda.set_device(rank)
else:
rank = 0
world_size = 1
# build model
if not osp.isfile(args.config):
try:
args.config = cfgs_name_path[args.config]
except KeyError:
raise FileNotFoundError(f'Cannot find {args.config}')
# load config
cfg = Config.fromfile(args.config)
# if args.cfg_options is not None:
# cfg.merge_from_dict(args.cfg_options)
model_name = cfg.model.type if isinstance(cfg.model.type,
str) else cfg.model.type.__name__
model = BUILDER.build(cfg.model)
backend = get_file_backend(args.pth_model)
# if os.path.exists(cfg.pretrained_pth):
# if isinstance(backend, PetrelBackend):
# from xtuner.utils.fileio import patch_fileio
# with patch_fileio():
# state_dict = guess_load_checkpoint(cfg.pretrained_pth)
# else:
# state_dict = guess_load_checkpoint(cfg.pretrained_pth)
#
# # del state_dict['llm.base_model.model.model.tok_embeddings.weight']
# model.load_state_dict(state_dict, strict=False)
# print(f'Load pre PTH model from {cfg.pretrained_pth}')
if isinstance(backend, PetrelBackend):
from xtuner.utils.fileio import patch_fileio
with patch_fileio():
state_dict = guess_load_checkpoint(args.pth_model)
else:
state_dict = guess_load_checkpoint(args.pth_model)
model.load_state_dict(state_dict, strict=False)
print(f'Load PTH model from {args.pth_model}')
datasets_configs = cfg.test_dataset
model.cuda()
# model.grounding_encoder.cuda()
# model.text_hidden_fcs.cuda()
model.eval()
dataset = RegionCap_Inference_Dataset(
image_folder='./1215_demos/mask_outs/out/',
metainfo=datasets_configs[0]['metainfo'],
# debug=True,
)
datasets = [dataset]
for i_dataset, dataset in enumerate(datasets):
model.preparing_for_generation(dataset.metainfo)
results = []
n_samples = len(dataset)
per_rank_samples = math.ceil(n_samples / world_size)
per_rank_ids = range(per_rank_samples * rank,
min(n_samples, per_rank_samples * (rank + 1)))
for idx in tqdm.tqdm(per_rank_ids):
data_batch = dataset[idx]
prediction = {'img_id': data_batch['img_id']}
outputs = model.predict_forward(**data_batch)
prediction.update(outputs)
# results.append(prediction)
text_output = outputs['prediction'].replace("<s>", "").replace("\n", "") \
.replace("region1", '').replace("Region1", '') \
.replace(':', '').replace(" ", " ").replace(" ", " ")
text_output = text_output.split("ASSISTANT: ")[-1]
cleaned_str = re.sub(r'<.*?>', '', text_output)
cleaned_str = cleaned_str.replace('[SEG]', '')
cleaned_str = ' '.join(cleaned_str.split()).strip("'")
cleaned_str = cleaned_str.strip()
result_dict = {}
result_dict["image_id"] = data_batch['img_id']
result_dict["caption"] = cleaned_str
result_dict["image_file"] = data_batch['image_file']
result_dict["prediction"] = cleaned_str
results.append(result_dict)
with open(os.path.join('./1215_demos/object_demos/', f"{data_batch['img_id']}.txt"), 'w') as f:
f.write(cleaned_str)
print(cleaned_str)
results = collect_results(results, n_samples)
if get_rank() == 0:
with open(args.output_path, 'w') as json_file:
json.dump(results, json_file, indent=2)
if __name__ == '__main__':
main()
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