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infer.py
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infer.py
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#Auth:lshzhang@tencent.com
#Implementation of FastBERT, paper refer:https://arxiv.org/pdf/2004.02178.pdf
import argparse
import json
import time
import logging
import torch
import torch.utils.data as data
import numpy as np
from tqdm import tqdm
from tensorboardX import SummaryWriter
from model_define.model_fastbert import FastBertModel, BertConfig
from data_utils.dataset_preparing import PrepareDataset, TextCollate
import torch.nn.functional as F
from utils import load_json_config, init_bert_adam_optimizer, load_saved_model, save_model, eval_pr
#随机数固定，RE-PRODUCIBLE
seed = 9999
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s")
debug_break = False
def infer_model(master_gpu_id, model, dataset,
use_cuda=False, num_workers=1, inference_speed=None, dump_info_file=None):
global global_step
global debug_break
model.eval()
infer_dataloader = data.DataLoader(dataset=dataset,
collate_fn=TextCollate(dataset),
pin_memory=use_cuda,
batch_size=1,
num_workers=num_workers,
shuffle=False)
correct_sum = 0
num_sample = infer_dataloader.dataset.__len__()
predicted_probs = []
true_labels = []
infos = []
logging.info("Inference Model...")
cnt = 0
stime_all = time.time()
for step, batch in enumerate(tqdm(infer_dataloader, unit="batch", ncols=100, desc="Inference process: ")):
texts = batch["texts"]
tokens = batch["tokens"].cuda(master_gpu_id) if use_cuda else batch["tokens"]
segment_ids = batch["segment_ids"].cuda(master_gpu_id) if use_cuda else batch["segment_ids"]
attn_masks = batch["attn_masks"].cuda(master_gpu_id) if use_cuda else batch["attn_masks"]
labels = batch["labels"].cuda(master_gpu_id) if use_cuda else batch["labels"]
with torch.no_grad():
probs, layer_idxes, uncertain_infos = model(tokens, token_type_ids=segment_ids, attention_mask=attn_masks,
inference=True, inference_speed=inference_speed)
_, top_index = probs.topk(1)
correct_sum += (top_index.view(-1) == labels).sum().item()
cnt += 1
if cnt == 1:
stime = time.time()
if dump_info_file != None:
for label, pred, prob, layer_i, text in zip(labels, top_index.view(-1), probs, [layer_idxes], texts):
infos.append((label.item(), pred.item(), prob.cpu().numpy(), layer_i, text))
if debug_break and step > 50:
break
time_per = (time.time() - stime)/(cnt - 1)
time_all = time.time() - stime_all
acc = format(correct_sum / num_sample, "0.4f")
logging.info("speed_arg:%s, time_per_record:%s, acc:%s, total_time:%s",
inference_speed, format(time_per, '0.4f'), acc, format(time_all, '0.4f'))
if dump_info_file != None and len(dump_info_file) != 0:
with open(dump_info_file, 'w') as fw:
for label, pred, prob, layer_i, text in infos:
fw.write('\t'.join([str(label), str(pred), str(layer_i), text])+'\n')
if probs.shape[1] == 2:
labels_pr = [info[0] for info in infos]
preds_pr = [info[1] for info in infos]
precise, recall = eval_pr(labels_pr, preds_pr)
logging.info("precise:%s, recall:%s", format(precise, '0.4f'), format(recall, '0.4f'))
def main(args):
config = load_json_config(args.model_config_file)
logging.info(json.dumps(config, indent=2, sort_keys=True))
logging.info("Load HyperParameters Done")
#---------------------MODEL GRAPH INIT--------------------------#
bert_config = BertConfig.from_json_file(config.get("bert_config_path"))
model = FastBertModel(bert_config, config)
load_saved_model(model, args.save_model_path)
logging.info(model)
logging.info("Initialize Model Done".center(60, "="))
#-----------GPU SETTING, INFER Only Support Max 1 GPU-----------#
use_cuda = args.gpu_ids != '-1'
if len(args.gpu_ids) == 1 and use_cuda:
master_gpu_id = int(args.gpu_ids)
model = model.cuda(int(args.gpu_ids)) if use_cuda else model
elif not use_cuda:
master_gpu_id = None
else:
raise RuntimeError("GPU Mode not support, INFER Only Support Max 1 GPU: " + args.gpu_ids)
#-----------------------Dataset Init---------------------------#
infer_dataset = PrepareDataset(vocab_file=config.get("vocab_file"),
max_seq_len=config.get("max_seq_len"),
num_class=config.get("num_class"),
data_file=args.infer_data)
logging.info("Load INFER Dataset Done, Total eval line: %s", infer_dataset.__len__())
#-----------------------Running Mode Start, Batch Size Only Support 1--------------------------------#
infer_model(master_gpu_id, model, infer_dataset,
use_cuda=use_cuda, num_workers=args.data_load_num_workers,
inference_speed=args.inference_speed, dump_info_file=args.dump_info_file)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Textclassification training script arguments.")
parser.add_argument("--model_config_file", dest="model_config_file", action="store",
help="The path of configuration json file.")
parser.add_argument("--save_model_path", dest="save_model_path", action="store",
help="The path of trained checkpoint model.")
parser.add_argument("--infer_data", dest="infer_data", action="store", help="")
parser.add_argument("--dump_info_file", dest="dump_info_file", action="store", help="")
parser.add_argument("--inference_speed", dest="inference_speed", action="store",
type=float, default=1.0, help="")
# -1 for NO GPU
parser.add_argument("--gpu_ids", dest="gpu_ids", action="store", default="0",
help="Device ids of used gpus, split by ',' , IF -1 then no gpu")
parser.add_argument("--data_load_num_workers", dest="data_load_num_workers", action="store",type=int, default=1, help="")
parser.add_argument("--debug_break", dest="debug_break", action="store", type=int, default=0,
help="Running debug_break, 0 or 1.")
parsed_args = parser.parse_args()
debug_break = (parsed_args.debug_break == 1)
main(parsed_args)