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infer_prune_model.py
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infer_prune_model.py
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'''
==================板块功能描述====================
@Time :2022/2/8 10:04
@Author : qiaofengsheng
@File :infer_prune_model.py
@Software :PyCharm
@description:
================================================
'''
'''
_*_coding:utf-8 _*_
@Time :2022/1/29 10:52
@Author : qiaofengsheng
@File :infer.py
@Software :PyCharm
'''
import os
from PIL import Image, ImageDraw, ImageFont
import cv2
import torch
from model.utils import utils
from torchvision import transforms
from model.net.net import *
import argparse
parse = argparse.ArgumentParser('infer models')
parse.add_argument('demo', type=str, help='推理类型支持：image/video/camera')
parse.add_argument('--prune_weights_path', type=str, default='', help='剪枝后的模型权重路径')
parse.add_argument('--image_path', type=str, default='', help='图片存放路径')
parse.add_argument('--video_path', type=str, default='', help='视频路径')
parse.add_argument('--camera_id', type=int, default=0, help='摄像头id')
class ModelInfer:
def __init__(self, config, weights_path):
self.config = config
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.transform = transforms.Compose([
transforms.ToTensor()
])
self.net = torch.load(weights_path)
self.net.eval()
def image_infer(self, image_path):
image = Image.open(image_path)
image_data = utils.keep_shape_resize(image, self.config['image_size'])
image_data = self.transform(image_data)
image_data = torch.unsqueeze(image_data, dim=0).to(self.device)
out = self.net(image_data)
out = torch.argmax(out)
result = self.config['class_names'][int(out)]
draw = ImageDraw.Draw(image)
font = ImageFont.truetype(r"C:\Windows\Fonts\BRITANIC.TTF", 35)
draw.text((10, 10), result, font=font, fill='red')
image.show()
def video_infer(self, video_path):
cap = cv2.VideoCapture(video_path)
while True:
_, frame = cap.read()
if _:
image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_data = Image.fromarray(image_data)
image_data = utils.keep_shape_resize(image_data, self.config['image_size'])
image_data = self.transform(image_data)
image_data = torch.unsqueeze(image_data, dim=0).to(self.device)
out = self.net(image_data)
out = torch.argmax(out)
result = self.config['class_names'][int(out)]
cv2.putText(frame, result, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), thickness=2)
cv2.imshow('frame', frame)
if cv2.waitKey(24) & 0XFF == ord('q'):
break
else:
break
def camera_infer(self, camera_id):
cap = cv2.VideoCapture(camera_id)
while True:
_, frame = cap.read()
h, w, c = frame.shape
if _:
image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_data = Image.fromarray(image_data)
image_data = utils.keep_shape_resize(image_data, self.config['image_size'])
image_data = self.transform(image_data)
image_data = torch.unsqueeze(image_data, dim=0).to(self.device)
out = self.net(image_data)
out = torch.argmax(out)
result = self.config['class_names'][int(out)]
cv2.putText(frame, result, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), thickness=2)
cv2.imshow('frame', frame)
if cv2.waitKey(24) & 0XFF == ord('q'):
break
else:
break
if __name__ == '__main__':
args = parse.parse_args()
config = utils.load_config_util('config/config.yaml')
model = ModelInfer(config, args.prune_weights_path)
if args.demo == 'image':
model.image_infer(args.image_path)
elif args.demo == 'video':
model.video_infer(args.video_path)
elif args.demo == 'camera':
model.camera_infer(args.camera_id)
else:
exit(0)