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predict.py
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predict.py
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import torch
from ANMT import ANMT
import argparse
from configs import config
import os
from utils.data_utilis import build_vocab,prep_image,build_reverse_vocab
import cv2
import numpy as np
from tqdm import tqdm
import json
def set_interact_args():
"""
Sets up the training arguments.
"""
parser = argparse.ArgumentParser()
# 数据路径
parser.add_argument('--vocab_path', default='./dict.txt', type=str, required=False, help='输入字典路径')
parser.add_argument('--data_dir', default='./AEC_recognition', type=str, required=False, help='输入测试数据路径')
# 训练参数
parser.add_argument('--max_seq', default=35, type=int, required=False, help='最大标签长度')
parser.add_argument('--device', default='cuda', type=str, required=False, help='运行设备')
parser.add_argument('--model_path',default='./model',type=str,required=False,help='模型位置')
# parser.add_argument('--multi_gpu',default='True',type=str,required=False,help='多gpu训练')
parser.add_argument('--local_rank',type=int)
parser.add_argument('--model_name',default='recognition_442_392.pth.tar',type=str,required=False,help='预模型名称')
parser.add_argument('--output_path',type=str,default='./result')
# parser.add_argument('--world_size',type=int,required=False,help='全局训练进程数')
return parser.parse_args()
def load_model(model,device):
'''
description: multi_gpu or single gpu
params:
@model{torch.nn.module}: defaut ANMT;
@device: cuda or cpu
@multi_gpu: more than one gpu or not;
return:
model
'''
if device == 'cuda':
if torch.cuda.device_count() > 1:
torch.distributed.init_process_group(backend="nccl")
local_rank = torch.distributed.get_rank()
torch.cuda.set_device(local_rank)
local_rank = 0
device = torch.device("cuda", local_rank)
model.to(device)
# model = torch.nn.DataParallel(model)
model = torch.nn.parallel.DistributedDataParallel(model,device_ids=[local_rank])
else:
device = torch.device("cuda")
model.to(device)
else:
device = torch.device("cpu")
model.to(device)
return model,device
def predict(model,images,reversed_vocab,device):
model.eval()
input_dim = 224
output = {}
for image in tqdm(images, total=len(images)):
image_name = image.split('\\')[-1]
temp = ''
image = np.array(cv2.imread(image))
image = prep_image(image,input_dim)
image = image.to(device)
predict_tokens = model(image,mode='predict')
for token in predict_tokens:
temp = temp + reversed_vocab[token]
output[image_name] = temp[:]
return output
def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
# train parameters
args = set_interact_args()
# model parameters
configs = config()
# params
embed_size = configs.embed_size
en_hidden_size = configs.en_hidden_size
de_hidden_size = configs.de_hidden_size
attention_size = configs.attention_size
height = configs.height
width = configs.width
feature_size = configs.feature_size
num_layers = configs.num_layers
ngpus_per_node = torch.cuda.device_count()
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
max_seq = args.max_seq
device = args.device
# data path
vocab_path = args.vocab_path
data_dir = args.data_dir
#construct the images path
image_data_dir = os.path.join(data_dir,'test','img')
images = [os.path.join(image_data_dir,image) for image in os.listdir(image_data_dir)]
# build vocab
vocab = build_vocab(vocab_path)
#build reversed vocab
reversed_vocab = build_reverse_vocab(vocab_path)
# construct the model
model = ANMT(height=height, width=width, input_channel=feature_size, embed_size=embed_size, en_hidden_size=en_hidden_size, de_hidden_size=de_hidden_size, attention_size=attention_size, vocab=vocab, max_seq=max_seq, num_layers=num_layers, device=device)
#the model save path
model_path = args.model_path
#the model name
model_name = args.model_name
# model_name = None
output_path = os.path.join(data_dir,'test',args.output_path)
if model_name != None:
pretrain_path = os.path.join(model_path,model_name)
model,device = load_model(model,device)
checkpoint = torch.load(pretrain_path)
state_dict = checkpoint['state_dict']
model.load_state_dict(state_dict)
if args.local_rank % ngpus_per_node == 0:
print('load success!')
else:
model,device = load_model(model,device)
# predict
output = predict(model=model,images=images,reversed_vocab=reversed_vocab,device=device)
if args.local_rank % ngpus_per_node == 0:
with open(os.path.join(output_path,'result.json'),'w') as f:
json.dump(output,f,indent=2)
print('finish')
if __name__ == '__main__':
main()