# -*- coding: utf-8 -*-
from PIL import Image
from collections import OrderedDict
import torch
import torch.nn.functional as F
import torchvision.models as models
import torchvision.transforms as transforms
from model_service.pytorch_model_service import PTServingBaseService
import time
from metric.metrics_manager import MetricsManager
import log
logger = log.getLogger(__name__)
input_size = 456
class ImageClassificationService(PTServingBaseService):
def __init__(self, model_name, model_path):
self.model_name = model_name
self.model_path = model_path
self.model = models.__dict__['resnet50'](num_classes=54)
self.use_cuda = False
if torch.cuda.is_available():
print('Using GPU for inference')
self.use_cuda = True
checkpoint = torch.load(self.model_path)
self.model = torch.nn.DataParallel(self.model).cuda()
self.model.load_state_dict(checkpoint['state_dict'])
else:
print('Using CPU for inference')
checkpoint = torch.load(self.model_path, map_location='cpu')
self.model.load_state_dict(state_dict)
self.model.eval()
self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
self.transforms = transforms.Compose([
transforms.Resize(input_size),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
self.normalize
])
self.label_id_name_dict = \
{
"0": "工艺品/仿唐三彩",
"1": "工艺品/仿宋木叶盏",
"2": "工艺品/布贴绣",
"3": "工艺品/景泰蓝",
"4": "工艺品/木马勺脸谱",
"5": "工艺品/柳编",
"6": "工艺品/葡萄花鸟纹银香囊",
"7": "工艺品/西安剪纸",
"8": "工艺品/陕历博唐妞系列",
"9": "景点/关中书院",
"10": "景点/兵马俑",
"11": "景点/南五台",
"12": "景点/大兴善寺",
"13": "景点/大观楼",
"14": "景点/大雁塔",
"15": "景点/小雁塔",
"16": "景点/未央宫城墙遗址",
"17": "景点/水陆庵壁塑",
"18": "景点/汉长安城遗址",
"19": "景点/西安城墙",
"20": "景点/钟楼",
"21": "景点/长安华严寺",
"22": "景点/阿房宫遗址",
"23": "民俗/唢呐",
"24": "民俗/皮影",
"25": "特产/临潼火晶柿子",
"26": "特产/山茱萸",
"27": "特产/玉器",
"28": "特产/阎良甜瓜",
"29": "特产/陕北红小豆",
"30": "特产/高陵冬枣",
"31": "美食/八宝玫瑰镜糕",
"32": "美食/凉皮",
"33": "美食/凉鱼",
"34": "美食/德懋恭水晶饼",
"35": "美食/搅团",
"36": "美食/枸杞炖银耳",
"37": "美食/柿子饼",
"38": "美食/浆水面",
"39": "美食/灌汤包",
"40": "美食/烧肘子",
"41": "美食/石子饼",
"42": "美食/神仙粉",
"43": "美食/粉汤羊血",
"44": "美食/羊肉泡馍",
"45": "美食/肉夹馍",
"46": "美食/荞面饸饹",
"47": "美食/菠菜面",
"48": "美食/蜂蜜凉粽子",
"49": "美食/蜜饯张口酥饺",
"50": "美食/西安油茶",
"51": "美食/贵妃鸡翅",
"52": "美食/醪糟",
"53": "美食/金线油塔"
}
def _preprocess(self, data):
preprocessed_data = {}
for k, v in data.items():
for file_name, file_content in v.items():
img = Image.open(file_content)
img = self.transforms(img)
preprocessed_data[k] = img
return preprocessed_data
def _inference(self, data):
img = data["input_img"]
img = img.unsqueeze(0)
if self.use_cuda:
img = img.cuda()
with torch.no_grad():
pred_score = self.model(img)
pred_score = F.softmax(pred_score.data, dim=1)
if pred_score is not None:
pred_label = torch.argsort(pred_score[0], descending=True)[:1][0].item()
pred_label = int(pred_label)
result = {'result': self.label_id_name_dict[str(pred_label)]}
else:
result = {'result': 'predict score is None'}
return result
def _postprocess(self, data):
return data
def inference(self, data):
"""
Wrapper function to run preprocess, inference and postprocess functions.
Parameters
----------
data : map of object
Raw input from request.
Returns
-------
list of outputs to be sent back to client.
data to be sent back
"""
pre_start_time = time.time()
data = self._preprocess(data)
infer_start_time = time.time()
# Update preprocess latency metric
pre_time_in_ms = (infer_start_time - pre_start_time) * 1000
logger.info('preprocess time: ' + str(pre_time_in_ms) + 'ms')
if self.model_name + '_LatencyPreprocess' in MetricsManager.metrics:
MetricsManager.metrics[self.model_name + '_LatencyPreprocess'].update(pre_time_in_ms)
data = self._inference(data)
infer_end_time = time.time()
infer_in_ms = (infer_end_time - infer_start_time) * 1000
logger.info('infer time: ' + str(infer_in_ms) + 'ms')
data = self._postprocess(data)
# Update inference latency metric
post_time_in_ms = (time.time() - infer_end_time) * 1000
logger.info('postprocess time: ' + str(post_time_in_ms) + 'ms')
if self.model_name + '_LatencyInference' in MetricsManager.metrics:
MetricsManager.metrics[self.model_name + '_LatencyInference'].update(post_time_in_ms)
# Update overall latency metric
if self.model_name + '_LatencyOverall' in MetricsManager.metrics:
MetricsManager.metrics[self.model_name + '_LatencyOverall'].update(pre_time_in_ms + post_time_in_ms)
logger.info('latency: ' + str(pre_time_in_ms + infer_in_ms + post_time_in_ms) + 'ms')
data['latency_time'] = pre_time_in_ms + infer_in_ms + post_time_in_ms
return data
