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main_cls.py
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main_cls.py
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import json
import logging
import os
from difflib import SequenceMatcher
from typing import Dict, List
import ml_collections.config_dict
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import wandb
from lhrs.CustomTrainer import init_distributed
from lhrs.CustomTrainer.utils import ConfigArgumentParser, setup_logger, str2bool
from lhrs.Dataset.build_loader import build_zero_shot_loader
from lhrs.Dataset.conversation import default_conversation
from lhrs.models import (
DEFAULT_IM_END_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_TOKEN,
IMAGE_TOKEN_INDEX,
build_model,
tokenizer_image_token,
)
from lhrs.utils import type_dict
from sklearn.metrics import balanced_accuracy_score, classification_report
from tqdm import tqdm
logger = logging.getLogger("train")
CLS_TEMPLATE = [lambda c: f"[CLS] Choose the best categories describe the image from: {c}"]
def find_index_of_max_similar_substring(given_string, string_list):
max_similarity = 0
max_index = -1
for i, string in enumerate(string_list):
similarity = (
SequenceMatcher(None, given_string, string)
.find_longest_match(0, len(given_string), 0, len(string))
.size
)
if similarity > max_similarity:
max_similarity = similarity
max_index = i
return max_index
def classname_2_idx(preds: List[str], classes_to_idx: Dict[str, int]):
results = []
classes = list(classes_to_idx.keys())
for pred in preds:
pred = pred.strip()
if pred in classes:
results.append(classes_to_idx[pred])
else:
index = find_index_of_max_similar_substring(pred, classes)
results.append(classes_to_idx[classes[index]])
return results
def parse_option():
parser = ConfigArgumentParser()
parser.add_argument(
"--opts",
help="Modify config options by adding 'KEY VALUE' pairs. ",
default=None,
nargs="+",
)
# basic
parser.add_argument("--batch-size", type=int, help="batch size for single GPU")
parser.add_argument("--data-path", type=str, help="path to dataset")
parser.add_argument("--workers", type=int, default=8, help="workers of dataloader")
parser.add_argument("--model-path", type=str, default=None, help="pretrained checkpoint path")
parser.add_argument("--enable-amp", type=str2bool, default=False, help="mixed precision")
parser.add_argument(
"--output",
default="output",
type=str,
metavar="PATH",
help="root of output folder, the full path is <output>/<model_name>/<tag> (default: output)",
)
parser.add_argument("--seed", type=int, default=322, help="random seed")
parser.add_argument(
"--use-checkpoint",
action="store_true",
help="whether to use gradient checkpointing to save memory",
)
parser.add_argument("--gpus", type=int, default=0, help="gpus ID")
parser.add_argument(
"--inf_sampler",
type=str2bool,
default=False,
help="Use Infinite loader if ture, else default datalodaer (Usually, inf_sampler for iterbased training)",
)
# wandb
parser.add_argument("--wandb", type=str2bool, default=False, help="wandb logger")
parser.add_argument("--entity", type=str, default="pumpkinn", help="wandb entity")
parser.add_argument("--project", type=str, default="MaskIndexNet", help="wandb project")
# HardWare
parser.add_argument(
"--accelerator",
default="cpu",
type=str,
choices=["cpu", "gpu", "mps"],
help="accelerator",
)
parser.add_argument("--local_rank", type=int, help="local rank")
config = parser.parse_args(wandb=True)
config = ml_collections.config_dict.ConfigDict(config)
return config
def main(config: ml_collections.ConfigDict):
logger.info(f"Creating model")
model = build_model(config, activate_modal=("rgb", "text"))
dtype = type_dict[config.dtype]
model.to(dtype)
data_loader_train = build_zero_shot_loader(config, mode="zero_shot_cls")
if config.model_path is not None:
logger.info(f"Loading pretrained checkpoint from {config.model_path}")
if getattr(model, "custom_load_state_dict", False):
msg = model.custom_load_state_dict(config.model_path)
else:
ckpt = torch.load(config.model_path, map_location="cpu")
msg = model.load_state_dict(ckpt["model"], strict=False)
if msg is not None:
logger.info(f"After loading, missing keys: {msg.missing_keys}, unexpected keys: {msg.unexpected_keys}")
logger.info(str(model))
if config.accelerator == "gpu":
if config.is_distribute:
device = torch.device(getattr(config, "local_rank", 0))
elif (
"CUDA_VISABLE_DEVICE" in os.environ.keys() and len(os.environ["CUDA_VISABLE_DEVICES"].split(",")) == 1
):
device = torch.device("cuda:" + os.environ["CUDA_VISABLE_DEVICES"])
else:
device = torch.device("cuda")
else:
device = torch.device(config.accelerator)
model.to(device)
model.eval()
if hasattr(data_loader_train.dataset, "classes"):
all_classes = data_loader_train.dataset.classes
else:
all_classes = data_loader_train.dataset.CLASS_NAME
all_classes = [i.lower().replace("_", " ") for i in all_classes]
classes_2_idx = {classname: idx for idx, classname in enumerate(all_classes)}
inp = CLS_TEMPLATE[0](all_classes)
conv = default_conversation.copy()
roles = conv.roles
if config.tune_im_start:
inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + inp
else:
inp = DEFAULT_IMAGE_TOKEN + "\n" + inp
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = (
tokenizer_image_token(prompt, model.text.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
.unsqueeze(0)
.to(device)
)
input_ids = input_ids.repeat(config.batch_size, 1)
model.eval()
with torch.no_grad():
preds = []
trues = []
for image, target in tqdm(data_loader_train, unit_scale=config.batch_size, desc="Evaluating"):
image = image.to(dtype).to(device)
if input_ids.shape[0] != image.shape[0]:
# last iter
input_ids = input_ids[: image.shape[0]]
with torch.autocast(
device_type="cuda" if config.accelerator == "gpu" else "cpu",
enabled=config.enable_amp,
dtype=dtype,
):
output_ids = model.generate(
input_ids=input_ids,
images=image,
do_sample=False,
num_beams=1,
temperature=1.0,
top_p=1.0,
max_new_tokens=20 if config.eval.dataset != "METERML" else 30,
)
outputs = model.text.tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds += outputs
trues.append(target.cpu())
preds = classname_2_idx(preds, classes_2_idx)
trues = torch.cat(trues)
mean_per_class_recall = balanced_accuracy_score(trues, preds)
logger.info(classification_report(trues, preds, digits=3, target_names=all_classes))
logger.info(mean_per_class_recall)
if __name__ == "__main__":
config = parse_option()
config.rank, config.local_rank, config.world_size = init_distributed()
config.is_distribute = config.world_size > 1
config.adjust_norm = False
print(config)
setup_logger("train", output=config.output, rank=config.rank)
os.makedirs(config.output, exist_ok=True)
if config.is_distribute:
seed = config.seed + dist.get_rank()
else:
seed = config.seed
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
if config.rank == 0:
path = os.path.join(config.output, "config.json")
with open(path, "w") as f:
configDict = dict(config.to_dict())
json.dump(configDict, f, indent=4)
logger.info(f"Full config saved to {path}")
logger.info(config)
if config.wandb and config.rank == 0:
wandb.init(config=config.to_dict(), entity=config.entity, project=config.project)
config = wandb.config
main(config)