训练日志如下
[2021/10/31 01:53:47] root INFO: [Train][Epoch 16/20][Iter: 0/4]lr: 0.00285, top1: 0.93750, top5: 0.96484, CELoss: 0.36489, loss: 0.36489, batch_cost: 1.48066s, reader_cost: 0.68550, ips: 172.89543 images/sec, eta: 0:00:29 [2021/10/31 01:53:49] root INFO: [Train][Epoch 16/20][Avg]top1: 0.95098, top5: 0.97745, CELoss: 0.31581, loss: 0.31581 [2021/10/31 01:53:53] root INFO: [Train][Epoch 17/20][Iter: 0/4]lr: 0.00183, top1: 0.94531, top5: 0.97656, CELoss: 0.32916, loss: 0.32916, batch_cost: 1.47958s, reader_cost: 0.68473, ips: 173.02266 images/sec, eta: 0:00:23 [2021/10/31 01:53:55] root INFO: [Train][Epoch 17/20][Avg]top1: 0.95686, top5: 0.98137, CELoss: 0.29560, loss: 0.29560 [2021/10/31 01:53:58] root INFO: [Train][Epoch 18/20][Iter: 0/4]lr: 0.00101, top1: 0.93750, top5: 0.98047, CELoss: 0.31542, loss: 0.31542, batch_cost: 1.47524s, reader_cost: 0.68058, ips: 173.53117 images/sec, eta: 0:00:17 [2021/10/31 01:54:01] root INFO: [Train][Epoch 18/20][Avg]top1: 0.94608, top5: 0.98627, CELoss: 0.29086, loss: 0.29086 [2021/10/31 01:54:04] root INFO: [Train][Epoch 19/20][Iter: 0/4]lr: 0.00042, top1: 0.97266, top5: 0.98438, CELoss: 0.24642, loss: 0.24642, batch_cost: 1.47376s, reader_cost: 0.67916, ips: 173.70590 images/sec, eta: 0:00:11 [2021/10/31 01:54:07] root INFO: [Train][Epoch 19/20][Avg]top1: 0.94608, top5: 0.97941, CELoss: 0.30998, loss: 0.30998 [2021/10/31 01:54:10] root INFO: [Train][Epoch 20/20][Iter: 0/4]lr: 0.00008, top1: 0.98047, top5: 0.98438, CELoss: 0.20209, loss: 0.20209, batch_cost: 1.47083s, reader_cost: 0.67647, ips: 174.05180 images/sec, eta: 0:00:05 [2021/10/31 01:54:13] root INFO: [Train][Epoch 20/20][Avg]top1: 0.95784, top5: 0.98922, CELoss: 0.25974, loss: 0.25974 [2021/10/31 01:54:16] root INFO: [Eval][Epoch 20][Iter: 0/4]CELoss: 0.47912, loss: 0.47912, top1: 0.91797, top5: 0.96094, batch_cost: 3.26175s, reader_cost: 3.02034, ips: 78.48538 images/sec [2021/10/31 01:54:17] root INFO: [Eval][Epoch 20][Avg]CELoss: 0.54982, loss: 0.54982, top1: 0.88922, top5: 0.96667 [2021/10/31 01:54:18] root INFO: Already save model in ./output/ResNet50_vd/best_model [2021/10/31 01:54:18] root INFO: [Eval][Epoch 20][best metric: 0.8892156844045601] [2021/10/31 01:54:18] root INFO: Already save model in ./output/ResNet50_vd/epoch_20 [2021/10/31 01:54:18] root INFO: Already save model in ./output/ResNet50_vd/latest
可见日志输出比较混乱，没有以前那么清晰，最好使用visualdl来查看训练情况
四、模型导出
!python tools/export_model.py \ -c ./ppcls/configs/quick_start/ResNet50_vd.yaml \ -o Global.pretrained_model=./output/ResNet50_vd/best_model \ -o Global.save_inference_dir=./deploy/models/class_ResNet50_vd_ImageNet_infer
[2022/04/04 18:13:38] root INFO: =========================================================== == PaddleClas is powered by PaddlePaddle ! == =========================================================== == == == For more info please go to the following website. == == == == https://github.com/PaddlePaddle/PaddleClas == =========================================================== [2022/04/04 18:13:38] root INFO: Arch : [2022/04/04 18:13:38] root INFO: name : ResNet50_vd [2022/04/04 18:13:38] root INFO: DataLoader : [2022/04/04 18:13:38] root INFO: Eval : [2022/04/04 18:13:38] root INFO: dataset : [2022/04/04 18:13:38] root INFO: cls_label_path : valid.txt [2022/04/04 18:13:38] root INFO: image_root : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/ [2022/04/04 18:13:38] root INFO: name : ImageNetDataset [2022/04/04 18:13:38] root INFO: transform_ops : [2022/04/04 18:13:38] root INFO: DecodeImage : [2022/04/04 18:13:38] root INFO: channel_first : False [2022/04/04 18:13:38] root INFO: to_rgb : True [2022/04/04 18:13:38] root INFO: ResizeImage : [2022/04/04 18:13:38] root INFO: resize_short : 256 [2022/04/04 18:13:38] root INFO: CropImage : [2022/04/04 18:13:38] root INFO: size : 224 [2022/04/04 18:13:38] root INFO: NormalizeImage : [2022/04/04 18:13:38] root INFO: mean : [0.485, 0.456, 0.406] [2022/04/04 18:13:38] root INFO: order : [2022/04/04 18:13:38] root INFO: scale : 1.0/255.0 [2022/04/04 18:13:38] root INFO: std : [0.229, 0.224, 0.225] [2022/04/04 18:13:38] root INFO: loader : [2022/04/04 18:13:38] root INFO: num_workers : 4 [2022/04/04 18:13:38] root INFO: use_shared_memory : True [2022/04/04 18:13:38] root INFO: sampler : [2022/04/04 18:13:38] root INFO: batch_size : 128 [2022/04/04 18:13:38] root INFO: drop_last : False [2022/04/04 18:13:38] root INFO: name : DistributedBatchSampler [2022/04/04 18:13:38] root INFO: shuffle : False [2022/04/04 18:13:38] root INFO: Train : [2022/04/04 18:13:38] root INFO: dataset : [2022/04/04 18:13:38] root INFO: cls_label_path : train.txt [2022/04/04 18:13:38] root INFO: image_root : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/ [2022/04/04 18:13:38] root INFO: name : ImageNetDataset [2022/04/04 18:13:38] root INFO: transform_ops : [2022/04/04 18:13:38] root INFO: DecodeImage : [2022/04/04 18:13:38] root INFO: channel_first : False [2022/04/04 18:13:38] root INFO: to_rgb : True [2022/04/04 18:13:38] root INFO: RandCropImage : [2022/04/04 18:13:38] root INFO: size : 224 [2022/04/04 18:13:38] root INFO: RandFlipImage : [2022/04/04 18:13:38] root INFO: flip_code : 1 [2022/04/04 18:13:38] root INFO: NormalizeImage : [2022/04/04 18:13:38] root INFO: mean : [0.485, 0.456, 0.406] [2022/04/04 18:13:38] root INFO: order : [2022/04/04 18:13:38] root INFO: scale : 1.0/255.0 [2022/04/04 18:13:38] root INFO: std : [0.229, 0.224, 0.225] [2022/04/04 18:13:38] root INFO: loader : [2022/04/04 18:13:38] root INFO: num_workers : 4 [2022/04/04 18:13:38] root INFO: use_shared_memory : True [2022/04/04 18:13:38] root INFO: sampler : [2022/04/04 18:13:38] root INFO: batch_size : 128 [2022/04/04 18:13:38] root INFO: drop_last : False [2022/04/04 18:13:38] root INFO: name : DistributedBatchSampler [2022/04/04 18:13:38] root INFO: shuffle : True [2022/04/04 18:13:38] root INFO: Global : [2022/04/04 18:13:38] root INFO: checkpoints : None [2022/04/04 18:13:38] root INFO: class_num : 102 [2022/04/04 18:13:38] root INFO: device : gpu [2022/04/04 18:13:38] root INFO: epochs : 20 [2022/04/04 18:13:38] root INFO: eval_during_train : True [2022/04/04 18:13:38] root INFO: eval_interval : 5 [2022/04/04 18:13:38] root INFO: image_shape : [3, 224, 224] [2022/04/04 18:13:38] root INFO: output_dir : ./output/ [2022/04/04 18:13:38] root INFO: pretrained_model : ./output/ResNet50_vd/best_model [2022/04/04 18:13:38] root INFO: print_batch_step : 10 [2022/04/04 18:13:38] root INFO: save_inference_dir : ./deploy/models/class_ResNet50_vd_ImageNet_infer [2022/04/04 18:13:38] root INFO: save_interval : 5 [2022/04/04 18:13:38] root INFO: use_visualdl : False [2022/04/04 18:13:38] root INFO: Infer : [2022/04/04 18:13:38] root INFO: PostProcess : [2022/04/04 18:13:38] root INFO: class_id_map_file : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/jpg/image_00030.jpg [2022/04/04 18:13:38] root INFO: name : Topk [2022/04/04 18:13:38] root INFO: topk : 5 [2022/04/04 18:13:38] root INFO: batch_size : 10 [2022/04/04 18:13:38] root INFO: infer_imgs : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/ [2022/04/04 18:13:38] root INFO: transforms : [2022/04/04 18:13:38] root INFO: DecodeImage : [2022/04/04 18:13:38] root INFO: channel_first : False [2022/04/04 18:13:38] root INFO: to_rgb : True [2022/04/04 18:13:38] root INFO: ResizeImage : [2022/04/04 18:13:38] root INFO: resize_short : 256 [2022/04/04 18:13:38] root INFO: CropImage : [2022/04/04 18:13:38] root INFO: size : 224 [2022/04/04 18:13:38] root INFO: NormalizeImage : [2022/04/04 18:13:38] root INFO: mean : [0.485, 0.456, 0.406] [2022/04/04 18:13:38] root INFO: order : [2022/04/04 18:13:38] root INFO: scale : 1.0/255.0 [2022/04/04 18:13:38] root INFO: std : [0.229, 0.224, 0.225] [2022/04/04 18:13:38] root INFO: ToCHWImage : None [2022/04/04 18:13:38] root INFO: Loss : [2022/04/04 18:13:38] root INFO: Eval : [2022/04/04 18:13:38] root INFO: CELoss : [2022/04/04 18:13:38] root INFO: weight : 1.0 [2022/04/04 18:13:38] root INFO: Train : [2022/04/04 18:13:38] root INFO: CELoss : [2022/04/04 18:13:38] root INFO: weight : 1.0 [2022/04/04 18:13:38] root INFO: Metric : [2022/04/04 18:13:38] root INFO: Eval : [2022/04/04 18:13:38] root INFO: TopkAcc : [2022/04/04 18:13:38] root INFO: topk : [1, 5] [2022/04/04 18:13:38] root INFO: Train : [2022/04/04 18:13:38] root INFO: TopkAcc : [2022/04/04 18:13:38] root INFO: topk : [1, 5] [2022/04/04 18:13:38] root INFO: Optimizer : [2022/04/04 18:13:38] root INFO: lr : [2022/04/04 18:13:38] root INFO: learning_rate : 0.0125 [2022/04/04 18:13:38] root INFO: name : Cosine [2022/04/04 18:13:38] root INFO: warmup_epoch : 5 [2022/04/04 18:13:38] root INFO: momentum : 0.9 [2022/04/04 18:13:38] root INFO: name : Momentum [2022/04/04 18:13:38] root INFO: regularizer : [2022/04/04 18:13:38] root INFO: coeff : 1e-05 [2022/04/04 18:13:38] root INFO: name : L2 [2022/04/04 18:13:38] root INFO: train with paddle 2.1.2 and device CUDAPlace(0) [2022/04/04 18:13:38] root WARNING: The Global.class_num will be deprecated. Please use Arch.class_num instead. Arch.class_num has been set to 102. W0404 18:13:38.957692 2099 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1 W0404 18:13:38.962862 2099 device_context.cc:422] device: 0, cuDNN Version: 7.6. /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return (isinstance(seq, collections.Sequence) and
!ls ./deploy/models/class_ResNet50_vd_ImageNet_infer -la
total 93944 drwxr-xr-x 2 aistudio aistudio 4096 Apr 4 18:13 . drwxr-xr-x 3 aistudio aistudio 4096 Apr 4 18:13 .. -rw-r--r-- 1 aistudio aistudio 95165295 Apr 4 18:13 inference.pdiparams -rw-r--r-- 1 aistudio aistudio 23453 Apr 4 18:13 inference.pdiparams.info -rw-r--r-- 1 aistudio aistudio 996386 Apr 4 18:13 inference.pdmodel
五、OpenVINO预测
鉴于AiStudio无法使用最新版OpenVINO，在本地跑完后上传
1.OpenVINO安装
此处要注意，使用最新版的OpenVINO，目前最新版为2022.1.0
!pip install OpenVINO
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: OpenVINO in c:\miniconda3\envs\p2g\lib\site-packages (2022.1.0) Requirement already satisfied: numpy<1.20,>=1.16.6 in c:\miniconda3\envs\p2g\lib\site-packages (from OpenVINO) (1.19.3)
2.Import
导入必须的OpenVINO库
# model download from pathlib import Path import os import urllib.request import tarfile # inference from openvino.runtime import Core # preprocessing import cv2 import numpy as np from openvino.preprocess import PrePostProcessor, ResizeAlgorithm from openvino.runtime import Layout, Type, AsyncInferQueue, PartialShape # results visualization import time import json from IPython.display import Image
3.预处理
3.1生成花分类字典
flowers_classes={} for i in range(102): flowers_classes[str(i)]='flower_'+ str(i) print(flowers_classes)
{'0': 'flower_0', '1': 'flower_1', '2': 'flower_2', '3': 'flower_3', '4': 'flower_4', '5': 'flower_5', '6': 'flower_6', '7': 'flower_7', '8': 'flower_8', '9': 'flower_9', '10': 'flower_10', '11': 'flower_11', '12': 'flower_12', '13': 'flower_13', '14': 'flower_14', '15': 'flower_15', '16': 'flower_16', '17': 'flower_17', '18': 'flower_18', '19': 'flower_19', '20': 'flower_20', '21': 'flower_21', '22': 'flower_22', '23': 'flower_23', '24': 'flower_24', '25': 'flower_25', '26': 'flower_26', '27': 'flower_27', '28': 'flower_28', '29': 'flower_29', '30': 'flower_30', '31': 'flower_31', '32': 'flower_32', '33': 'flower_33', '34': 'flower_34', '35': 'flower_35', '36': 'flower_36', '37': 'flower_37', '38': 'flower_38', '39': 'flower_39', '40': 'flower_40', '41': 'flower_41', '42': 'flower_42', '43': 'flower_43', '44': 'flower_44', '45': 'flower_45', '46': 'flower_46', '47': 'flower_47', '48': 'flower_48', '49': 'flower_49', '50': 'flower_50', '51': 'flower_51', '52': 'flower_52', '53': 'flower_53', '54': 'flower_54', '55': 'flower_55', '56': 'flower_56', '57': 'flower_57', '58': 'flower_58', '59': 'flower_59', '60': 'flower_60', '61': 'flower_61', '62': 'flower_62', '63': 'flower_63', '64': 'flower_64', '65': 'flower_65', '66': 'flower_66', '67': 'flower_67', '68': 'flower_68', '69': 'flower_69', '70': 'flower_70', '71': 'flower_71', '72': 'flower_72', '73': 'flower_73', '74': 'flower_74', '75': 'flower_75', '76': 'flower_76', '77': 'flower_77', '78': 'flower_78', '79': 'flower_79', '80': 'flower_80', '81': 'flower_81', '82': 'flower_82', '83': 'flower_83', '84': 'flower_84', '85': 'flower_85', '86': 'flower_86', '87': 'flower_87', '88': 'flower_88', '89': 'flower_89', '90': 'flower_90', '91': 'flower_91', '92': 'flower_92', '93': 'flower_93', '94': 'flower_94', '95': 'flower_95', '96': 'flower_96', '97': 'flower_97', '98': 'flower_98', '99': 'flower_99', '100': 'flower_100', '101': 'flower_101'}
3.2预处理callback定义
def callback(infer_request, i) -> None: """ Define the callback function for postprocessing :param: infer_request: the infer_request object i: the iteration of inference :retuns: None """ # flowers_classes predictions = next(iter(infer_request.results.values())) indices = np.argsort(-predictions[0]) if (i == 0): # Calculate the first inference time latency = time.time() - start print(f"latency: {latency}") for n in range(5): print( "class name: {}, probability: {:.5f}" .format(flowers_classes[str(list(indices)[n])], predictions[0][list(indices)[n]]) )
3.3读取模型
# Intialize Inference Engine with Core() ie = Core() # model_path model_path="inference/inference.pdmodel" model = ie.read_model(model_path) # get the information of intput and output layer input_layer = model.input(0) output_layer = model.output(0)
4.调用API进行预处理
- 如果输入数据不完全符合模型输入张量，则需要额外的操作/步骤将数据转换为模型所期望的格式。这些操作被称为“预处理”。
- 预处理步骤被集成到执行图中，并在选定的设备(CPU/GPU/VPU/等)上执行，而不是总是在CPU上执行。这将提高所选设备的利用率。
相关 API: docs.openvino.ai/latest/open…
# 待预测图片 filename = "myflower.jpg" test_image = cv2.imread(filename) test_image = np.expand_dims(test_image, 0) / 255 _, h, w, _ = test_image.shape # 调整模型输入图片尺寸 model.reshape({input_layer.any_name: PartialShape([1, 3, 224, 224])}) ppp = PrePostProcessor(model) # 设置输入 tensor 信息: # - input() 提供模型的输入 # - 数据格式 "NHWC" # - 设置静态模型输入维度 ppp.input().tensor() \ .set_spatial_static_shape(h, w) \ .set_layout(Layout("NHWC")) inputs = model.inputs # 设模型有“NCHW”布局作为输入 ppp.input().model().set_layout(Layout("NCHW")) # 处理操作: # - tensor RESIZE_LINEAR 缩放设置 # - 每个通道的归一化 # - 将每个像素数据划分为适当的比例值 ppp.input().preprocess() \ .resize(ResizeAlgorithm.RESIZE_LINEAR, 224, 224) \ .mean([0.485, 0.456, 0.406]) \ .scale([0.229, 0.224, 0.225]) # 设置输出张量信息: # - 张量精度设置为 'f32' ppp.output().tensor().set_element_type(Type.f32) # Apply preprocessing to modify the original 'model' model = ppp.build()
5.预测
使用“AUTO”作为设备名，委托OpenVINO选择设备。自动设备插件内部识别和选择设备从英特尔的CPU和GPU之间依赖于设备功能和模型(例如，精度)的特点。然后，它将推理请求分配给最佳设备。
AUTO立即在CPU上启动推理，然后在准备好后透明地转移到GPU(或VPU)，大大减少了第一次推理的时间。
# 检查可用设备 devices = ie.available_devices for device in devices: device_name = ie.get_property(device_name=device, name="FULL_DEVICE_NAME") print(f"{device}: {device_name}") # 将模型加载到由AUTO从可用设备列表中选择的设备 compiled_model = ie.compile_model(model=model, device_name="AUTO") # 创建请求队列 infer_queue = AsyncInferQueue(compiled_model) infer_queue.set_callback(callback) start = time.time() # 开始预测 infer_queue.start_async({input_layer.any_name: test_image}, 0) infer_queue.wait_all() Image(filename=filename)
CPU: Intel(R) Core(TM) i5-9400F CPU @ 2.90GHz latency: 0.02329254150390625 class name: flower_76, probability: 0.40075 class name: flower_81, probability: 0.15170 class name: flower_91, probability: 0.03979 class name: flower_12, probability: 0.03356 class name: flower_17, probability: 0.02347
6.性能技巧:延迟和吞吐量
吞吐量和延迟是一些最广泛使用的度量应用程序整体性能的指标。
- 延迟 是预测单个输入所需要的时间(ms)
- 吞吐量, 处理时间/处理的输入数
OpenVINO性能提示是在考虑可移植性的情况下配置性能的新方法。性能提示将允许设备自己配置，而不是将应用程序需要映射到低级别的性能设置，并保持一个相关的应用程序逻辑来分别配置每个可能的设备。
高级技巧: docs.openvino.ai/latest/open…
6.1延迟计算
可以通过配置调整应用程序的性能设置，让设备调整以实现更好的面向延迟的性能。
loop = 100 # AUTO sets device config based on hints compiled_model = ie.compile_model(model=model, device_name="AUTO", config={"PERFORMANCE_HINT": "LATENCY"}) infer_queue = AsyncInferQueue(compiled_model) # implement AsyncInferQueue Python API to boost the performance in Async mode infer_queue.set_callback(callback) start = time.time() # run infernce for 100 times to get the average FPS for i in range(loop): infer_queue.start_async({input_layer.any_name: test_image}, i) infer_queue.wait_all() end = time.time() # Calculate the average FPS fps = loop / (end - start) print(f"fps: {fps}")
latency: 0.018686771392822266 class name: flower_76, probability: 0.40075 class name: flower_81, probability: 0.15170 class name: flower_91, probability: 0.03979 class name: flower_12, probability: 0.03356 class name: flower_17, probability: 0.02347 fps: 50.20953840260486
6.2吞吐量计算
可以使用配置设置应用程序的性能设置，让设备调整以实现更好的吞吐量性能。
# AUTO sets device config based on hints compiled_model = ie.compile_model(model=model, device_name="AUTO", config={"PERFORMANCE_HINT": "THROUGHPUT"}) infer_queue = AsyncInferQueue(compiled_model) infer_queue.set_callback(callback) start = time.time() for i in range(loop): infer_queue.start_async({input_layer.any_name: test_image}, i) infer_queue.wait_all() end = time.time() # Calculate the average FPS fps = loop / (end - start) print(f"fps: {fps}")
latency: 0.04830741882324219 class name: flower_76, probability: 0.40075 class name: flower_81, probability: 0.15170 class name: flower_91, probability: 0.03979 class name: flower_12, probability: 0.03356 class name: flower_17, probability: 0.02347 fps: 57.274455164002134
!benchmark_app -m $model_path -data_shape [1,3,224,224] -hint "latency"
[Step 1/11] Parsing and validating input arguments [ WARNING ] -nstreams default value is determined automatically for a device. Although the automatic selection usually provides a reasonable performance, but it still may be non-optimal for some cases, for more information look at README. [Step 2/11] Loading OpenVINO [ INFO ] OpenVINO: API version............. 2022.1.0-7019-cdb9bec7210-releases/2022/1 [ INFO ] Device info CPU openvino_intel_cpu_plugin version 2022.1 Build................... 2022.1.0-7019-cdb9bec7210-releases/2022/1 [Step 3/11] Setting device configuration [Step 4/11] Reading network files [ INFO ] Read model took 144.60 ms [Step 5/11] Resizing network to match image sizes and given batch [ INFO ] Network batch size: ? [Step 6/11] Configuring input of the model [ INFO ] Model input 'x' precision u8, dimensions ([N,C,H,W]): ? 3 224 224 [ INFO ] Model output 'save_infer_model/scale_0.tmp_1' precision f32, dimensions ([...]): ? 102 [Step 7/11] Loading the model to the device [ INFO ] Compile model took 232.62 ms [Step 8/11] Querying optimal runtime parameters [ INFO ] DEVICE: CPU [ INFO ] AVAILABLE_DEVICES , [''] [ INFO ] RANGE_FOR_ASYNC_INFER_REQUESTS , (1, 1, 1) [ INFO ] RANGE_FOR_STREAMS , (1, 6) [ INFO ] FULL_DEVICE_NAME , Intel(R) Core(TM) i5-9400F CPU @ 2.90GHz [ INFO ] OPTIMIZATION_CAPABILITIES , ['FP32', 'FP16', 'INT8', 'BIN', 'EXPORT_IMPORT'] [ INFO ] CACHE_DIR , [ INFO ] NUM_STREAMS , 1 [ INFO ] INFERENCE_NUM_THREADS , 0 [ INFO ] PERF_COUNT , False [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS , 0 [Step 9/11] Creating infer requests and preparing input data [ INFO ] Create 1 infer requests took 0.00 ms [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values! [ INFO ] Fill input 'x' with random values [Step 10/11] Measuring performance (Start inference asynchronously, 1 inference requests, inference only: False, limits: 60000 ms duration) [ INFO ] Benchmarking in full mode (inputs filling are included in measurement loop). [ INFO ] First inference took 31.26 ms [Step 11/11] Dumping statistics report Count: 2793 iterations Duration: 60018.45 ms Latency: AVG: 21.40 ms MIN: 17.28 ms MAX: 73.23 ms Throughput: 46.54 FPS
