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from mmdet.datasets import RefCocoDataset
from mmdet.datasets.transforms import LoadAnnotations
from mmdet.evaluation import RefSegMetric
import argparse
from mmengine.config import Config
from xtuner.model.utils import guess_load_checkpoint
from xtuner.registry import BUILDER
from xtuner.utils.constants import DEFAULT_IMAGE_TOKEN
from accelerate import Accelerator
from accelerate.utils import gather_object
from mmdet.structures.mask import BitmapMasks
from mmcv.transforms import LoadImageFromFile
from tqdm import tqdm
import torch
import torch.nn.functional as F
from time import time
from projects.f_llm.datasets.transforms import PILLoadImageFromFile, RefCOCO2PNG
from projects.lisa.datasets.refcoco_segm_dataset import ReferSegmDataset
from projects.glamm.datasets.collate_fns.glamm_collate_fn import glamm_collate_fn
from third_parts.segment_anything.utils.transforms import ResizeLongestSide
from pycocotools import mask as mask_utils
from projects.glamm.datasets.utils.utils import SEG_QUESTIONS, ANSWER_LIST
extra_image_processor = ResizeLongestSide(
target_length=1024,
)
import copy
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from xtuner.utils import PROMPT_TEMPLATE
template = PROMPT_TEMPLATE.phi3_chat
_system = '你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。'
# _system = 'You are an AI assistant whose name is Phi-3.'
_system = ''
begin_str = f'{DEFAULT_IMAGE_TOKEN}\n'
template['INSTRUCTION'] = '<|user|>\n{input}<|end|><|assistant|>\n'
transformer = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((448, 448), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
])
def get_inputid_labels(conversations, image_token_str):
input = ''
out_conversation = []
while conversations and conversations[0]['from'] == 'gpt':
# Skip the first one if it is from gpt
conversations = conversations[1:]
for msg in conversations:
if msg['from'] == 'human':
if image_token_str is None and '<image>' in msg['value']:
msg['value'] = msg['value'].replace('<image>', '')
if '<image>' in msg['value']:
msg['value'] = msg['value'].replace('<image>', image_token_str).strip()
input += msg['value'].strip()
elif msg['from'] == 'gpt':
out_conversation.append({
'input': input,
'output': msg['value'].strip()
})
input = ''
else:
raise NotImplementedError
input_ids, labels = [], []
for i, single_turn_conversation in enumerate(out_conversation):
input = single_turn_conversation.get('input', '')
if input is None:
input = ''
input_text = template.INSTRUCTION.format(
input=input, round=i + 1)
if i == 0:
if _system != '' and _system is not None:
system = template.SYSTEM.format(system=_system)
input_text = system + input_text
input_encode = tokenizer.encode(input_text, add_special_tokens=True)
else:
input_encode = tokenizer.encode(input_text, add_special_tokens=False)
input_ids += input_encode
labels += [-100] * len(input_encode)
output_text = single_turn_conversation.get('output', '')
if template.get('SUFFIX', None):
output_text += template.SUFFIX
output_encode = tokenizer.encode(
output_text, add_special_tokens=False)
input_ids += output_encode
labels += copy.deepcopy(output_encode)
max_length = 8192
if len(input_ids) > max_length:
input_ids = input_ids[:max_length]
labels = labels[:max_length]
return input_ids, labels
if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('config', help='config file path.')
parser.add_argument('--checkpoint', default=None, type=str)
args = parser.parse_args()
# Initialize accelerator
accelerator = Accelerator()
# each GPU creates a string
message = [f"Hello this is GPU {accelerator.process_index}"]
# collect the messages from all GPUs
messages = gather_object(message)
# output the messages only on the main process with accelerator.print()
accelerator.print(messages)
cfg = Config.fromfile(args.config)
tokenizer = cfg.tokenizer
tokenizer = BUILDER.build(tokenizer)
tokenizer.add_tokens(['[SEG]'], special_tokens=True)
model = BUILDER.build(cfg.model)
if args.checkpoint is not None:
state_dict = guess_load_checkpoint(args.checkpoint)
missing, unexpected = model.load_state_dict(state_dict, strict=False)
accelerator.print(f"Unexpected parameters: {unexpected}")
model = model.to(device=accelerator.device)
model.eval()
model.to(torch.bfloat16)
dataset = RefCocoDataset(
data_root='data/coco/',
data_prefix=dict(img_path='train2014/'),
text_mode='select_first',
ann_file='refcoco/instances.json',
split_file='refcoco/refs(unc).p',
split='val'
)
accelerator.wait_for_everyone()
data_ids = list(range(len(dataset)))
results = []
from PIL import Image
import numpy as np
from projects.lisa.datasets.sem_seg_dataset import dynamic_preprocess
with accelerator.split_between_processes(data_ids) as sub_ids:
for idx in tqdm(sub_ids, disable=not accelerator.is_main_process):
ann_info = dataset[idx]
image = Image.open(ann_info['img_path']).convert('RGB')
width, height = image.size
g_image = np.array(image) # for grounding
g_image = extra_image_processor.apply_image(g_image)
g_pixel_values = torch.from_numpy(g_image).permute(2, 0, 1).contiguous()
images = dynamic_preprocess(image, 1, 12, 448, True)
pixel_values = [transformer(image) for image in images]
pixel_values = torch.stack(pixel_values)
patch_token = int((448 // 14)**2 * (0.5**2))
num_image_tokens = pixel_values.shape[0] * patch_token
image_token_str = f'<img>' + '<IMG_CONTEXT>' * num_image_tokens+ '</img>'
instances, phrases = ann_info['instances'], ann_info['text']
for inst, phrase in zip(instances, phrases):
if '.' == phrase[-1]:
phrase = phrase[:-1]
binary_mask = np.zeros((height, width), dtype=np.uint8)
for seg in inst["mask"]:
rles = mask_utils.frPyObjects([seg], height, width)
m = mask_utils.decode(rles)
m = m.astype(np.uint8)
binary_mask += m.squeeze()
import random
conversation = []
question = random.choice(SEG_QUESTIONS).format(class_name=phrase)
question = begin_str + question
conversation.append({'from':'human', 'value': question})
conversation.append({'from':'gpt', 'value': ''})
input_ids, labels = get_inputid_labels(conversation, image_token_str)
input_ids = input_ids[:-1] # remove <|end|>
out_data_dict = {
'input_ids': torch.tensor(input_ids),
'labels': torch.tensor(labels),
'g_pixel_values': g_pixel_values,
'pixel_values': pixel_values,
'masks': binary_mask[None],
}
data_sample = glamm_collate_fn([out_data_dict])
with torch.no_grad():
outputs = model(**data_sample, mode='predict')
gt_masks = binary_mask[None] > 0
pred_mask_logits = outputs['pred_mask_logits']
if pred_mask_logits is None:
pred_masks = torch.zeros_like(gt_masks)
else:
pred_masks = pred_mask_logits.sigmoid().cpu() > 0.5
assert len(pred_masks) == len(gt_masks)
mask_cnt = pred_masks.shape[0]
results.append(
dict(
pred_instances=dict(masks=pred_masks),
gt_masks=BitmapMasks(
masks=gt_masks,
height=gt_masks.shape[1],
width=gt_masks.shape[2]))
)
results = gather_object(results)
if accelerator.is_main_process:
accelerator.print(
f"Collected {len(results)} result samples from all gpus")
evaluator = RefSegMetric(metric=['cIoU', 'mIoU'])
evaluator.process(data_batch=dict(), data_samples=results)
metrics = evaluator.compute_metrics(evaluator.results)
accelerator.print(f"Evaluation results on : {metrics}")
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