模型推理
运行命令：python inference.py

–-coding:utf-8-–

import os
import argparse
import torch
import torch.nn as nn
import numpy as np
import time
import datetime
import os
import math
from datetime import datetime
import cv2
import torch.nn.functional as F
from models.resnet import resnet18,resnet34,resnet50,resnet101
from models.squeezenet import squeezenet1_1,squeezenet1_0
from models.shufflenetv2 import ShuffleNetV2
from models.shufflenet import ShuffleNet
from models.mobilenetv2 import MobileNetV2
from torchvision.models import shufflenet_v2_x1_5 ,shufflenet_v2_x1_0 , shufflenet_v2_x2_0
from models.rexnetv1 import ReXNetV1
from utils.common_utils import *
import copy
from hand_data_iter.datasets import draw_bd_handpose
if name == “__main__”:

parser = argparse.ArgumentParser(description='Project Hand Pose Inference')
parser.add_argument('--model_path', type=str, default = './w/resnet50_2021-418.pth',
    help = 'model_path') # 模型门路
parser.add_argument('--model', type=str, default = 'resnet_50',
    help = '''model : resnet_34,resnet_50,resnet_101,squeezenet1_0,squeezenet1_1,shufflenetv2,shufflenet,mobilenetv2
        shufflenet_v2_x1_5 ,shufflenet_v2_x1_0 , shufflenet_v2_x2_0,ReXNetV1''') # 模型类型
parser.add_argument('--num_classes', type=int , default = 42,
    help = 'num_classes') #  手部 21 关键点，(x,y)*2 = 42
parser.add_argument('--GPUS', type=str, default = '0',
    help = 'GPUS') # GPU 抉择
parser.add_argument('--test_path', type=str, default = './image/',
    help = 'test_path') # 测试图片门路
parser.add_argument('--img_size', type=tuple , default = (256,256),
    help = 'img_size') # 输出模型图片尺寸
parser.add_argument('--vis', type=bool , default = True,
    help = 'vis') # 是否可视化图片
print('\n/******************* {} ******************/\n'.format(parser.description))
#--------------------------------------------------------------------------
ops = parser.parse_args()# 解析增加参数
#--------------------------------------------------------------------------
print('----------------------------------')
unparsed = vars(ops) # parse_args()办法的返回值为 namespace，用 vars()内建函数化为字典
for key in unparsed.keys():
    print('{} : {}'.format(key,unparsed[key]))
#---------------------------------------------------------------------------
os.environ['CUDA_VISIBLE_DEVICES'] = ops.GPUS
test_path =  ops.test_path # 测试图片文件夹门路
#---------------------------------------------------------------- 构建模型
print('use model : %s'%(ops.model))
if ops.model == 'resnet_50':
    model_ = resnet50(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == 'resnet_18':
    model_ = resnet18(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == 'resnet_34':
    model_ = resnet34(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == 'resnet_101':
    model_ = resnet101(num_classes = ops.num_classes,img_size=ops.img_size[0])
elif ops.model == "squeezenet1_0":
    model_ = squeezenet1_0(num_classes=ops.num_classes)
elif ops.model == "squeezenet1_1":
    model_ = squeezenet1_1(num_classes=ops.num_classes)
elif ops.model == "shufflenetv2":
    model_ = ShuffleNetV2(ratio=1., num_classes=ops.num_classes)
elif ops.model == "shufflenet_v2_x1_5":
    model_ = shufflenet_v2_x1_5(pretrained=False,num_classes=ops.num_classes)
elif ops.model == "shufflenet_v2_x1_0":
    model_ = shufflenet_v2_x1_0(pretrained=False,num_classes=ops.num_classes)
elif ops.model == "shufflenet_v2_x2_0":
    model_ = shufflenet_v2_x2_0(pretrained=False,num_classes=ops.num_classes)
elif ops.model == "shufflenet":
    model_ = ShuffleNet(num_blocks = [2,4,2], num_classes=ops.num_classes, groups=3)
elif ops.model == "mobilenetv2":
    model_ = MobileNetV2(num_classes=ops.num_classes)
elif ops.model == "ReXNetV1":
    model_ = ReXNetV1(width_mult=1.0, depth_mult=1.0, num_classes=ops.num_classes)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
model_ = model_.to(device)
model_.eval() # [电子钱包](https://www.gendan5.com/wallet.html)设置为前向推断模式
# print(model_)# 打印模型构造
# 加载测试模型
if os.access(ops.model_path,os.F_OK):# checkpoint
    chkpt = torch.load(ops.model_path, map_location=device)
    model_.load_state_dict(chkpt)
    print('load test model : {}'.format(ops.model_path))
#---------------------------------------------------------------- 预测图片
''' 倡议 检测手 bbox 后，crop 手图片的预处理形式：# img 为原图
x_min,y_min,x_max,y_max,score = bbox
w_ = max(abs(x_max-x_min),abs(y_max-y_min))
w_ = w_*1.1
x_mid = (x_max+x_min)/2
y_mid = (y_max+y_min)/2
x1,y1,x2,y2 = int(x_mid-w_/2),int(y_mid-w_/2),int(x_mid+w_/2),int(y_mid+w_/2)
x1 = np.clip(x1,0,img.shape[1]-1)
x2 = np.clip(x2,0,img.shape[1]-1)
y1 = np.clip(y1,0,img.shape[0]-1)
y2 = np.clip(y2,0,img.shape[0]-1)
'''
with torch.no_grad():
    idx = 0
    for file in os.listdir(ops.test_path):
        if '.jpg' not in file:
            continue
        idx += 1
        print('{}) image : {}'.format(idx,file))
        img = cv2.imread(ops.test_path + file)
        img_width = img.shape[1]
        img_height = img.shape[0]
        # 输出图片预处理
        img_ = cv2.resize(img, (ops.img_size[1],ops.img_size[0]), interpolation = cv2.INTER_CUBIC)
        img_ = img_.astype(np.float32)
        img_ = (img_-128.)/256.
        img_ = img_.transpose(2, 0, 1)
        img_ = torch.from_numpy(img_)
        img_ = img_.unsqueeze_(0)
        if use_cuda:
            img_ = img_.cuda()  # (bs, 3, h, w)
        pre_ = model_(img_.float()) # 模型推理
        output = pre_.cpu().detach().numpy()
        output = np.squeeze(output)
        pts_hand = {} #构建关键点连线可视化构造
        for i in range(int(output.shape[0]/2)):
            x = (output[i*2+0]*float(img_width))
            y = (output[i*2+1]*float(img_height))
            pts_hand[str(i)] = {}
            pts_hand[str(i)] = {
                "x":x,
                "y":y,
                }
        draw_bd_handpose(img,pts_hand,0,0) # 绘制关键点连线
        #------------- 绘制关键点
        for i in range(int(output.shape[0]/2)):
            x = (output[i*2+0]*float(img_width))
            y = (output[i*2+1]*float(img_height))
            cv2.circle(img, (int(x),int(y)), 3, (255,50,60),-1)
            cv2.circle(img, (int(x),int(y)), 1, (255,150,180),-1)
        if ops.vis:
            cv2.namedWindow('image',0)
            cv2.imshow('image',img)
            cv2.imwrite(file, img)
            if cv2.waitKey(600) == 27 :
                break
cv2.destroyAllWindows()
print('well done')