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49b555e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import os
from torch import multiprocessing as mp
from torchvision.utils import save_image
from argparse import ArgumentParser
import time
import multiprocessing
from dataset_tools.multi_gpu_infer_with_prompt import _run_on_multiple_gpus
from absl import logging
from utils import set_logger
if __name__ == "__main__":
# set start method as 'spawn' to avoid CUDA re-initialization issues
multiprocessing.set_start_method('spawn')
parser = ArgumentParser()
parser.add_argument("-v", "--verbose", action="store_true")
parser.add_argument("--multiprocess", action="store_true")
parser.add_argument("--gpu_ids", type=lambda x: [int(i) for i in x.split(",")], default=[0])
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory to store the cache files."
)
parser.add_argument(
"--node_id",
type=int,
default=0,
help="Node ID for distributed inference."
)
parser.add_argument(
"--node_ids",
type=lambda x: [int(i) for i in x.split(",")],
default=[0],
help="Node IDs for distributed inference, separated by commas."
)
parser.add_argument(
"--dataset_name",
type=str,
default="coco",
)
parser.add_argument(
"--dataset_anno_file",
type=str,
default="./data/prompts/captions_val2017.json",
)
parser.add_argument(
"--model_name",
type=str,
default="leloy/Anole-7b-v0.1-hf",
)
parser.add_argument(
"--max_num_new_tokens",
type=int,
default=16,
)
parser.add_argument(
"--multi_token_init_scheme",
type=str,
default='random', # sample_horizon sample_vertical # '2d_repeat' 'random' #'2d_extrapolation', #'repeat_last' # '1d_extrapolation'
)
parser.add_argument(
"--seed",
type=int,
default=42,
)
parser.add_argument(
"--image_top_k",
type=int,
default=2000,
)
parser.add_argument(
"--target_size",
type=int,
default=1024,
)
parser.add_argument(
"--prefix_token_sampler_scheme",
type=str,
default='speculative_jacobi',
)
parser.add_argument(
"--guidance_scale",
type=float,
default=3.0,
)
parser.add_argument(
"--output_dir",
type=str,
default="/home/leihaodong/AAAI25/exp/SJD",
)
parser.add_argument(
"--temperature",
type=float,
default=1.0,
)
parser.add_argument(
"--num_images",
type=int,
default=1,
)
parser.add_argument(
"--tokenizer_path",
default='/data/lei/localmodel/lumina_mgpt/chameleon/tokenizer',
type=str, help="location of the reference images for evaluation"
)
parser.add_argument("--return_accl",default=True,type=bool)
args = parser.parse_args()
start_time = time.time()
max_num_new_tokens = args.max_num_new_tokens
multi_token_init_scheme = args.multi_token_init_scheme
seed = args.seed if args.seed >=0 else None
model_name = args.model_name
dataset_name = args.dataset_name
guidance_scale = args.guidance_scale #3.0
image_top_k = args.image_top_k
prefix_token_sampler_scheme = args.prefix_token_sampler_scheme
num_images = args.num_images
if args.target_size > 0:
target_size = args.target_size
else:
potential_target_size = model_name.split("-")[-1]
if potential_target_size.isdigit():
target_size = int(potential_target_size)
else:
target_size = 512
workdir = args.output_dir
if not os.path.exists(workdir):
os.makedirs(workdir)
set_logger(log_level='info', fname=os.path.join(workdir, 'gen_img_output.log'))
logging.info(f"cache dir: {args.cache_dir}")
logging.info(f"gpu_ids: {args.gpu_ids}")
logging.info(f"node_ids: {args.node_ids}")
logging.info(f"target_size: {target_size}")
_run_on_multiple_gpus(
gpu_ids=args.gpu_ids,
node_ids=args.node_ids,
node_id=args.node_id,
\
dataset_params = dict(
name = args.dataset_name,
annFile = args.dataset_anno_file,
data_len = num_images
),
model_name = args.model_name,
\
cache_dir = args.cache_dir,
target_size = target_size,
seed = seed,
max_num_new_tokens = max_num_new_tokens,
multi_token_init_scheme = multi_token_init_scheme,
guidance_scale = guidance_scale,
image_top_k=image_top_k,
max_gen_len=8192,
temperature=args.temperature,
output_dir = workdir,
prefix_token_sampler_scheme = prefix_token_sampler_scheme,
local_chameleon_tokenizer_path = args.tokenizer_path,
return_accl = args.return_accl
)
end_time = time.time()
logging.info(f"Total Time taken: {end_time - start_time}") |