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general_inference.py
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general_inference.py
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import argparse
import os
import warnings
import cv2
import torch
import torch.nn.parallel
import torch.utils.data
from loguru import logger
from typing import List, Union
import utils.config as config
from model import build_segmenter
from utils.simple_tokenizer import SimpleTokenizer as _Tokenizer
import torch.nn.functional as F
import numpy as np
warnings.filterwarnings("ignore")
cv2.setNumThreads(0)
_tokenizer = _Tokenizer()
def tokenize(texts: Union[str, List[str]],
context_length: int = 77,
truncate: bool = False) -> torch.LongTensor:
"""
Returns the tokenized representation of given input string(s)
Parameters
----------
texts : Union[str, List[str]]
An input string or a list of input strings to tokenize
context_length : int
The context length to use; all CLIP models use 77 as the context length
truncate: bool
Whether to truncate the text in case its encoding is longer than the context length
Returns
-------
A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
"""
if isinstance(texts, str):
texts = [texts]
sot_token = _tokenizer.encoder["<|startoftext|>"]
eot_token = _tokenizer.encoder["<|endoftext|>"]
all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token]
for text in texts]
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
for i, tokens in enumerate(all_tokens):
if len(tokens) > context_length:
if truncate:
tokens = tokens[:context_length]
tokens[-1] = eot_token
else:
raise RuntimeError(
f"Input {texts[i]} is too long for context length {context_length}"
)
result[i, :len(tokens)] = torch.tensor(tokens)
return result
def convert(img):
mean = torch.tensor([0.48145466, 0.4578275,
0.40821073]).reshape(3, 1, 1)
std = torch.tensor([0.26862954, 0.26130258,
0.27577711]).reshape(3, 1, 1)
# Image ToTensor & Normalize
img = torch.from_numpy(img.transpose((2, 0, 1)))
if not isinstance(img, torch.FloatTensor):
img = img.float()
img.div_(255.).sub_(mean).div_(std)
return img
def getTransformMat(img_size, input_size, inverse=False):
ori_h, ori_w = img_size
inp_h, inp_w = input_size
scale = min(inp_h / ori_h, inp_w / ori_w)
new_h, new_w = ori_h * scale, ori_w * scale
bias_x, bias_y = (inp_w - new_w) / 2., (inp_h - new_h) / 2.
src = np.array([[0, 0], [ori_w, 0], [0, ori_h]], np.float32)
dst = np.array([[bias_x, bias_y], [new_w + bias_x, bias_y],
[bias_x, new_h + bias_y]], np.float32)
mat = cv2.getAffineTransform(src, dst)
if inverse:
mat_inv = cv2.getAffineTransform(dst, src)
return mat, mat_inv
return mat, None
def run_demo(args, model, img, expression, save_path, ori_img, params):
model.eval()
# data
img = img.cuda(non_blocking=True)
sent = expression
text = tokenize(sent, args.word_len, True)
text = text.cuda(non_blocking=True)
# inference
img = img.unsqueeze(0)
pred = model(img, text)
if pred.shape[-2:] != img.shape[-2:]:
pred = F.interpolate(pred,
size=img.shape[-2:],
mode='bilinear',
align_corners=True).squeeze()
pred = torch.sigmoid(pred)
h, w = params['ori_size']
mat = params['inverse']
pred = pred.cpu().numpy()
pred = cv2.warpAffine(pred, mat, (w, h),
flags=cv2.INTER_CUBIC,
borderValue=0.)
pred = np.array(pred > 0.35)
# Matting Image
# mat_img = ori_img * pred[:,:,None]
# cv2.imwrite(filename=save_path.replace('demo', 'demo_matting'), img=mat_img)
# Mask
pred = np.array(pred*255, dtype=np.uint8)
cv2.imwrite(filename=save_path, img=pred)
def get_parser():
parser = argparse.ArgumentParser(
description='Pytorch Referring Expression Segmentation')
parser.add_argument('--config',
default='config/general_inference.yaml',
type=str,
help='config file')
parser.add_argument('--img',
type=str,
help='imgs path')
parser.add_argument('--exp',
type=str,
help='expression of target objects')
parser.add_argument('--sp',
type=str,
default='demo.png',
help='save path')
parser.add_argument('--opts',
default=None,
nargs=argparse.REMAINDER,
help='override some settings in the config.')
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
if args.opts is not None:
cfg = config.merge_cfg_from_list(cfg, args.opts)
cfg.img = args.img
cfg.exp = args.exp
cfg.sp = args.sp
return cfg
@logger.catch
def main():
args = get_parser()
args.input_size = (args.input_size, args.input_size)
# build model
model, _ = build_segmenter(args)
model = torch.nn.DataParallel(model).cuda()
logger.info(model)
if os.path.isfile(args.model_dir):
logger.info("=> loading checkpoint '{}'".format(args.model_dir))
checkpoint = torch.load(args.model_dir)
model.load_state_dict(checkpoint['state_dict'], strict=True)
logger.info("=> loaded checkpoint '{}'".format(args.model_dir))
else:
raise ValueError(
"=> resume failed! no checkpoint found at '{}'. Please check args.resume again!"
.format(args.model_dir))
image_path = args.img
text_prompt = args.exp
ori_img = cv2.imread(image_path)
img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
img_size = img.shape[:2]
mat, mat_inv = getTransformMat(img_size, args.input_size, True)
img = cv2.warpAffine(
img,
mat,
args.input_size,
flags=cv2.INTER_CUBIC,
borderValue=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255])
img = convert(img)
save_path = args.sp
params = {
'inverse': mat_inv,
'ori_size': np.array(img_size)
}
_ = run_demo(args, model, img, text_prompt, save_path, ori_img, params)
if __name__ == '__main__':
main()