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The PaddlePaddle-based model has been trained and the next step is to use OpenVINO for predictive deployment

1. Use PaddleClas for model training
2. Download inference static model
3. Deployment reasoning in the OpenVINO environment

One, flower recognition

1. Introduction to the data set

The Oxford 102 Flowers Dataset is a flower collection Dataset, mainly used for image classification, which is divided into 102 types of Flowers in total, and each type contains 40 to 258 images.

This dataset was published in 2008 by the Department of Engineering Science at the University of Oxford with a paper called Automated Flower Classification over a Large Number of classes.

TXT (training set, 1020 images), valid. TXT (validation set, 1020 images), and test.txt(6149) have been generated in the folder. Format of each line in the file: label_id of the image relative to the path image (note: space in between).

2. PaddleClas profile

PaddleClas is currently release2.3, so it’s a bit different than before, so you need to get used to it again.

Address: gitee.com/paddlepaddl…

Configs has been moved to the PPCLS directory to deploy as a separate deploy directory

3.OpenVINO 2022.1 Deployment support

OpenVINO™ is an open source AI predictive deployment toolkit that supports multiple formats, is paddle-friendly and currently requires no conversion.

4.OpenVINO 2022.1 Workflow

Unzip the dataset! tar -xvf data/data19852/flowers102.tar -C ./data/ >log.logCopy the code

PaddleClas

# Download the latest version! git clone https://gitee.com/paddlepaddle/PaddleClas/ --depth=1
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%cd PaddleClas/ ! pip install -r requirements.txt >log.logCopy the code

/home/aistudio/PaddleClas
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3. Model training

1. Modify imagenet_dataset. Py

Directory: \ PPCLS \ data \ dataloader \ imagenet_dataset py

There is a bug in the directory section.

assert os.path.exists(self._cls_path)
assert os.path.exists(self._img_root)

add

self._cls_path=os.path.join(self._img_root,self._cls_path)

Otherwise, relative paths cannot be used

class ImageNetDataset(CommonDataset): def _load_anno(self, seed=None): If cls_path uses a relative directory, an error will be reported. Join (self._img_root,self._cls_path) # assert os.path.exists(self._cls_path) # assert os.path.exists(self._img_root) self._cls_path=os.path.join(self._img_root,self._cls_path) print('self._cls_path',self._cls_path) self.images = [] self.labels = [] with open(self._cls_path) as fd: lines = fd.readlines() if seed is not None: np.random.RandomState(seed).shuffle(lines) for l in lines: l = l.strip().split(" ") self.images.append(os.path.join(self._img_root, l[0])) self.labels.append(int(l[1])) assert os.path.exists(self.images[-1])Copy the code

2. Modify the configuration file

# global configs Global: checkpoints: null Pretrained_model: null output_dir:./output/ # CPU configuration # eval_during_train: True # eval_interval: Print_batch_step: 10 # Use visualDL use_visualDL: False # used for static mode and model export image_shape: [3, 224, 224] # save address save_inference_dir: ./inference # model architecture Arch: name: ResNet50_vd # loss function config for traing/eval process Loss: Train: -celoss: weight: 1.0 Eval: -celoss: weight: 1.0 Optimizer: name: Momentum Momentum: 0.9 LR: name: Cosup_epoch: 5 Regularizer: name: 'L2' Coeff: 0.00001 # data loader for train and eval DataLoader: train: dataset: name: ImageNetDataset image_root: /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/ cls_label_path: train.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - RandCropImage: size: 224 - RandFlipImage: flip_code: 1 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] STD: [0.229, 0.224, 0.225] Order: 'sampler: name' DistributedBatchSampler batch_size: 256 drop_last: False shuffle: True loader: num_workers: 4 use_shared_memory: True Eval: dataset: name: ImageNetDataset image_root: /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/ cls_label_path: valid.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] STD: [0.229, 0.224, 0.225] Order: 'sampler: name' DistributedBatchSampler batch_size: 256 drop_last: False shuffle: False loader: num_workers: 4 use_shared_memory: True Infer: infer_imgs: /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/ batch_size: 10 transforms: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224-NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] STD: [0.229, 0.224, 0.225] order: "-tochwImage: PostProcess: name: Topk topk: 5 class_id_map_file: /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/jpg/image_00030.jpg Metric: Train: - TopkAcc: topk: [1, 5] Eval: - TopkAcc: topk: [1, 5]Copy the code

The -c parameter specifies the path of the training configuration file. For details about the hyperparameters, see the YAML file
Yaml global. device parameter is set to CPU, that is, CPU is used for training (if not set, this parameter defaults to True).
If epochs is set to 20 in the YAML file, it indicates that 20 epoch iterations are performed on the entire data set. The training time is expected to be about 20 minutes (the training time varies with cpus). In this case, the training model is not sufficient. If the training model accuracy is improved, please set this parameter as large as 40, and the training time will be extended accordingly

3. Configuration description

3.1 Global Configuration (Global)

Parameter name	Specific meaning	The default value	An optional value
checkpoints	Breakpoint model path for recovery training	null	str
pretrained_model	Pretrain model path	null	str
output_dir	Save model path	“./output/”	str
save_interval	How many epochs do you save the model at	1	int
eval_during_train	Whether to evaluate during training	True	bool
eval_interval	How many epochs were used to evaluate the model	1	int
epochs	Train the total epoch number		int
print_batch_step	How many mini-batches are printed every	10	int
use_visualdl	Whether to use VisualDL to visualize the training process	False	bool
image_shape	Image size	[3，224，224]	list, shape: (3,)
save_inference_dir	Preservation path of the Inference model	“./inference”	str
eval_mode	The pattern of eval	“classification”	“retrieval”

3.2 structure (Arch)

Parameter name	Specific meaning	The default value	An optional value
name	Model structure name	ResNet50	PaddleClas provides the model structure
class_num	Class number	1000	int
pretrained	Pretraining model	False	Bool, STR

3.3 Loss Function (Loss)

Parameter name	Specific meaning	The default value	An optional value
CELoss	Cross entropy loss function	–	–
CELoss.weight	The weight of CELoss in the whole Loss	1.0	float
CELoss.epsilon	Epsilon value of label_smooth in CELoss	0.1	Float between 0 and 1

3.4 Optimizer

Parameter name	Specific meaning	The default value	An optional value
name	Optimizer method name	“Momentum”	“RmsProp” and other optimizers
momentum	Momentum values	0.9	float
lr.name	Ways of learning rate decline	“Cosine”	“Linear”, “Piecewise” and other descent methods
lr.learning_rate	Initial value of learning rate	0.1	float
lr.warmup_epoch	Warmup round number	0	Int, such as 5
regularizer.name	Regularize method name	“L2”	[“L1”, “L2”]
regularizer.coeff	Regularization coefficient	0.00007	float

4. Training

! pwd ! cp ~/ResNet50_vd.yaml ./ppcls/configs/quick_start/ResNet50_vd.yaml ! cp ~/imagenet_dataset.py ./ppcls/data/dataloader/imagenet_dataset.pyCopy the code

/home/aistudio/PaddleClas
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# GPU set! export CUDA_VISIBLE_DEVICES=0

# -o Arch. Pretrained =True # -o Arch. Pretrained =True! python tools/train.py -c ./ppcls/configs/quick_start/ResNet50_vd.yaml -o Arch.pretrained=True
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A new filed (pretrained) detected!
[2022/04/04 17:51:03] root INFO: 
===========================================================
==        PaddleClas is powered by PaddlePaddle !        ==
===========================================================
==                                                       ==
==   For more info please go to the following website.   ==
==                                                       ==
==       https://github.com/PaddlePaddle/PaddleClas      ==
===========================================================

[2022/04/04 17:51:03] root INFO: Arch : 
[2022/04/04 17:51:03] root INFO:     name : ResNet50_vd
[2022/04/04 17:51:03] root INFO:     pretrained : True
[2022/04/04 17:51:03] root INFO: DataLoader : 
[2022/04/04 17:51:03] root INFO:     Eval : 
[2022/04/04 17:51:03] root INFO:         dataset : 
[2022/04/04 17:51:03] root INFO:             cls_label_path : valid.txt
[2022/04/04 17:51:03] root INFO:             image_root : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/
[2022/04/04 17:51:03] root INFO:             name : ImageNetDataset
[2022/04/04 17:51:03] root INFO:             transform_ops : 
[2022/04/04 17:51:03] root INFO:                 DecodeImage : 
[2022/04/04 17:51:03] root INFO:                     channel_first : False
[2022/04/04 17:51:03] root INFO:                     to_rgb : True
[2022/04/04 17:51:03] root INFO:                 ResizeImage : 
[2022/04/04 17:51:03] root INFO:                     resize_short : 256
[2022/04/04 17:51:03] root INFO:                 CropImage : 
[2022/04/04 17:51:03] root INFO:                     size : 224
[2022/04/04 17:51:03] root INFO:                 NormalizeImage : 
[2022/04/04 17:51:03] root INFO:                     mean : [0.485, 0.456, 0.406]
[2022/04/04 17:51:03] root INFO:                     order : 
[2022/04/04 17:51:03] root INFO:                     scale : 1.0/255.0
[2022/04/04 17:51:03] root INFO:                     std : [0.229, 0.224, 0.225]
[2022/04/04 17:51:03] root INFO:         loader : 
[2022/04/04 17:51:03] root INFO:             num_workers : 4
[2022/04/04 17:51:03] root INFO:             use_shared_memory : True
[2022/04/04 17:51:03] root INFO:         sampler : 
[2022/04/04 17:51:03] root INFO:             batch_size : 128
[2022/04/04 17:51:03] root INFO:             drop_last : False
[2022/04/04 17:51:03] root INFO:             name : DistributedBatchSampler
[2022/04/04 17:51:03] root INFO:             shuffle : False
[2022/04/04 17:51:03] root INFO:     Train : 
[2022/04/04 17:51:03] root INFO:         dataset : 
[2022/04/04 17:51:03] root INFO:             cls_label_path : train.txt
[2022/04/04 17:51:03] root INFO:             image_root : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/
[2022/04/04 17:51:03] root INFO:             name : ImageNetDataset
[2022/04/04 17:51:03] root INFO:             transform_ops : 
[2022/04/04 17:51:03] root INFO:                 DecodeImage : 
[2022/04/04 17:51:03] root INFO:                     channel_first : False
[2022/04/04 17:51:03] root INFO:                     to_rgb : True
[2022/04/04 17:51:03] root INFO:                 RandCropImage : 
[2022/04/04 17:51:03] root INFO:                     size : 224
[2022/04/04 17:51:03] root INFO:                 RandFlipImage : 
[2022/04/04 17:51:03] root INFO:                     flip_code : 1
[2022/04/04 17:51:03] root INFO:                 NormalizeImage : 
[2022/04/04 17:51:03] root INFO:                     mean : [0.485, 0.456, 0.406]
[2022/04/04 17:51:03] root INFO:                     order : 
[2022/04/04 17:51:03] root INFO:                     scale : 1.0/255.0
[2022/04/04 17:51:03] root INFO:                     std : [0.229, 0.224, 0.225]
[2022/04/04 17:51:03] root INFO:         loader : 
[2022/04/04 17:51:03] root INFO:             num_workers : 4
[2022/04/04 17:51:03] root INFO:             use_shared_memory : True
[2022/04/04 17:51:03] root INFO:         sampler : 
[2022/04/04 17:51:03] root INFO:             batch_size : 128
[2022/04/04 17:51:03] root INFO:             drop_last : False
[2022/04/04 17:51:03] root INFO:             name : DistributedBatchSampler
[2022/04/04 17:51:03] root INFO:             shuffle : True
[2022/04/04 17:51:03] root INFO: Global : 
[2022/04/04 17:51:03] root INFO:     checkpoints : None
[2022/04/04 17:51:03] root INFO:     class_num : 102
[2022/04/04 17:51:03] root INFO:     device : gpu
[2022/04/04 17:51:03] root INFO:     epochs : 20
[2022/04/04 17:51:03] root INFO:     eval_during_train : True
[2022/04/04 17:51:03] root INFO:     eval_interval : 5
[2022/04/04 17:51:03] root INFO:     image_shape : [3, 224, 224]
[2022/04/04 17:51:03] root INFO:     output_dir : ./output/
[2022/04/04 17:51:03] root INFO:     pretrained_model : None
[2022/04/04 17:51:03] root INFO:     print_batch_step : 10
[2022/04/04 17:51:03] root INFO:     save_inference_dir : ./inference
[2022/04/04 17:51:03] root INFO:     save_interval : 5
[2022/04/04 17:51:03] root INFO:     use_visualdl : False
[2022/04/04 17:51:03] root INFO: Infer : 
[2022/04/04 17:51:03] root INFO:     PostProcess : 
[2022/04/04 17:51:03] root INFO:         class_id_map_file : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/jpg/image_00030.jpg
[2022/04/04 17:51:03] root INFO:         name : Topk
[2022/04/04 17:51:03] root INFO:         topk : 5
[2022/04/04 17:51:03] root INFO:     batch_size : 10
[2022/04/04 17:51:03] root INFO:     infer_imgs : /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/
[2022/04/04 17:51:03] root INFO:     transforms : 
[2022/04/04 17:51:03] root INFO:         DecodeImage : 
[2022/04/04 17:51:03] root INFO:             channel_first : False
[2022/04/04 17:51:03] root INFO:             to_rgb : True
[2022/04/04 17:51:03] root INFO:         ResizeImage : 
[2022/04/04 17:51:03] root INFO:             resize_short : 256
[2022/04/04 17:51:03] root INFO:         CropImage : 
[2022/04/04 17:51:03] root INFO:             size : 224
[2022/04/04 17:51:03] root INFO:         NormalizeImage : 
[2022/04/04 17:51:03] root INFO:             mean : [0.485, 0.456, 0.406]
[2022/04/04 17:51:03] root INFO:             order : 
[2022/04/04 17:51:03] root INFO:             scale : 1.0/255.0
[2022/04/04 17:51:03] root INFO:             std : [0.229, 0.224, 0.225]
[2022/04/04 17:51:03] root INFO:         ToCHWImage : None
[2022/04/04 17:51:03] root INFO: Loss : 
[2022/04/04 17:51:03] root INFO:     Eval : 
[2022/04/04 17:51:03] root INFO:         CELoss : 
[2022/04/04 17:51:03] root INFO:             weight : 1.0
[2022/04/04 17:51:03] root INFO:     Train : 
[2022/04/04 17:51:03] root INFO:         CELoss : 
[2022/04/04 17:51:03] root INFO:             weight : 1.0
[2022/04/04 17:51:03] root INFO: Metric : 
[2022/04/04 17:51:03] root INFO:     Eval : 
[2022/04/04 17:51:03] root INFO:         TopkAcc : 
[2022/04/04 17:51:03] root INFO:             topk : [1, 5]
[2022/04/04 17:51:03] root INFO:     Train : 
[2022/04/04 17:51:03] root INFO:         TopkAcc : 
[2022/04/04 17:51:03] root INFO:             topk : [1, 5]
[2022/04/04 17:51:03] root INFO: Optimizer : 
[2022/04/04 17:51:03] root INFO:     lr : 
[2022/04/04 17:51:03] root INFO:         learning_rate : 0.0125
[2022/04/04 17:51:03] root INFO:         name : Cosine
[2022/04/04 17:51:03] root INFO:         warmup_epoch : 5
[2022/04/04 17:51:03] root INFO:     momentum : 0.9
[2022/04/04 17:51:03] root INFO:     name : Momentum
[2022/04/04 17:51:03] root INFO:     regularizer : 
[2022/04/04 17:51:03] root INFO:         coeff : 1e-05
[2022/04/04 17:51:03] root INFO:         name : L2
[2022/04/04 17:51:03] root INFO: profiler_options : None
[2022/04/04 17:51:03] root INFO: train with paddle 2.1.2 and device CUDAPlace(0)
[2022/04/04 17:51:03] root WARNING: The Global.class_num will be deprecated. Please use Arch.class_num instead. Arch.class_num has been set to 102.
self._cls_path /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/train.txt
self._cls_path /home/aistudio/data/oxford-102-flowers/oxford-102-flowers/valid.txt
[2022/04/04 17:51:03] root WARNING: 'TopkAcc' metric can not be used when setting 'batch_transform_ops' in config. The 'TopkAcc' metric has been removed.
W0404 17:51:03.718078   846 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0404 17:51:03.723338   846 device_context.cc:422] device: 0, cuDNN Version: 7.6.
[2022/04/04 17:51:08] root INFO: unique_endpoints {''}
[2022/04/04 17:51:08] root INFO: Found /home/aistudio/.paddleclas/weights/ResNet50_vd_pretrained.pdparams
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1301: UserWarning: Skip loading for fc.weight. fc.weight receives a shape [2048, 1000], but the expected shape is [2048, 102].
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1301: UserWarning: Skip loading for fc.bias. fc.bias receives a shape [1000], but the expected shape is [102].
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
[2022/04/04 17:51:09] root WARNING: The training strategy in config files provided by PaddleClas is based on 4 gpus. But the number of gpus is 1 in current training. Please modify the stategy (learning rate, batch size and so on) if use config files in PaddleClas to train.
[2022/04/04 17:51:12] root INFO: [Train][Epoch 1/20][Iter: 0/8]lr: 0.00031, CELoss: 4.64081, loss: 4.64081, batch_cost: 3.14893s, reader_cost: 2.57331, ips: 40.64869 images/sec, eta: 0:08:23
[2022/04/04 17:51:16] root INFO: [Train][Epoch 1/20][Avg]CELoss: 4.64772, loss: 4.64772
[2022/04/04 17:51:16] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:51:19] root INFO: [Train][Epoch 2/20][Iter: 0/8]lr: 0.00281, CELoss: 4.61437, loss: 4.61437, batch_cost: 1.05037s, reader_cost: 0.63829, ips: 121.86160 images/sec, eta: 0:02:39
[2022/04/04 17:51:22] root INFO: [Train][Epoch 2/20][Avg]CELoss: 4.58869, loss: 4.58869
[2022/04/04 17:51:23] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:51:26] root INFO: [Train][Epoch 3/20][Iter: 0/8]lr: 0.00531, CELoss: 4.55260, loss: 4.55260, batch_cost: 1.19191s, reader_cost: 0.78459, ips: 107.39076 images/sec, eta: 0:02:51
[2022/04/04 17:51:30] root INFO: [Train][Epoch 3/20][Avg]CELoss: 4.45869, loss: 4.45869
[2022/04/04 17:51:30] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:51:33] root INFO: [Train][Epoch 4/20][Iter: 0/8]lr: 0.00781, CELoss: 4.32414, loss: 4.32414, batch_cost: 0.95354s, reader_cost: 0.56087, ips: 134.23697 images/sec, eta: 0:02:09
[2022/04/04 17:51:36] root INFO: [Train][Epoch 4/20][Avg]CELoss: 4.16781, loss: 4.16781
[2022/04/04 17:51:37] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:51:39] root INFO: [Train][Epoch 5/20][Iter: 0/8]lr: 0.01031, CELoss: 3.81278, loss: 3.81278, batch_cost: 0.97704s, reader_cost: 0.57012, ips: 131.00846 images/sec, eta: 0:02:05
[2022/04/04 17:51:43] root INFO: [Train][Epoch 5/20][Avg]CELoss: 3.64121, loss: 3.64121
[2022/04/04 17:51:46] root INFO: [Eval][Epoch 5][Iter: 0/8]CELoss: 3.10458, loss: 3.10458, top1: 0.62500, top5: 0.84375, batch_cost: 2.88314s, reader_cost: 2.69916, ips: 44.39597 images/sec
[2022/04/04 17:51:49] root INFO: [Eval][Epoch 5][Avg]CELoss: 3.06763, loss: 3.06763, top1: 0.58627, top5: 0.83039
[2022/04/04 17:51:49] root INFO: Already save model in ./output/ResNet50_vd/best_model
[2022/04/04 17:51:49] root INFO: [Eval][Epoch 5][best metric: 0.586274507933972]
[2022/04/04 17:51:50] root INFO: Already save model in ./output/ResNet50_vd/epoch_5
[2022/04/04 17:51:50] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:51:53] root INFO: [Train][Epoch 6/20][Iter: 0/8]lr: 0.01250, CELoss: 3.23097, loss: 3.23097, batch_cost: 1.12798s, reader_cost: 0.72043, ips: 113.47702 images/sec, eta: 0:02:15
[2022/04/04 17:51:56] root INFO: [Train][Epoch 6/20][Avg]CELoss: 2.82732, loss: 2.82732
[2022/04/04 17:51:57] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:00] root INFO: [Train][Epoch 7/20][Iter: 0/8]lr: 0.01233, CELoss: 2.31577, loss: 2.31577, batch_cost: 0.97315s, reader_cost: 0.58090, ips: 131.53180 images/sec, eta: 0:01:48
[2022/04/04 17:52:03] root INFO: [Train][Epoch 7/20][Avg]CELoss: 2.02123, loss: 2.02123
[2022/04/04 17:52:04] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:06] root INFO: [Train][Epoch 8/20][Iter: 0/8]lr: 0.01189, CELoss: 1.60169, loss: 1.60169, batch_cost: 0.96181s, reader_cost: 0.56401, ips: 133.08299 images/sec, eta: 0:01:40
[2022/04/04 17:52:10] root INFO: [Train][Epoch 8/20][Avg]CELoss: 1.38111, loss: 1.38111
[2022/04/04 17:52:10] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:13] root INFO: [Train][Epoch 9/20][Iter: 0/8]lr: 0.01121, CELoss: 1.10794, loss: 1.10794, batch_cost: 1.00229s, reader_cost: 0.59248, ips: 127.70792 images/sec, eta: 0:01:36
[2022/04/04 17:52:16] root INFO: [Train][Epoch 9/20][Avg]CELoss: 0.93859, loss: 0.93859
[2022/04/04 17:52:17] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:20] root INFO: [Train][Epoch 10/20][Iter: 0/8]lr: 0.01031, CELoss: 0.78641, loss: 0.78641, batch_cost: 1.16687s, reader_cost: 0.75940, ips: 109.69474 images/sec, eta: 0:01:42
[2022/04/04 17:52:23] root INFO: [Train][Epoch 10/20][Avg]CELoss: 0.66623, loss: 0.66623
[2022/04/04 17:52:27] root INFO: [Eval][Epoch 10][Iter: 0/8]CELoss: 0.75807, loss: 0.75807, top1: 0.85938, top5: 0.95312, batch_cost: 3.28170s, reader_cost: 3.07438, ips: 39.00414 images/sec
[2022/04/04 17:52:29] root INFO: [Eval][Epoch 10][Avg]CELoss: 0.66149, loss: 0.66149, top1: 0.89118, top5: 0.97647
[2022/04/04 17:52:30] root INFO: Already save model in ./output/ResNet50_vd/best_model
[2022/04/04 17:52:30] root INFO: [Eval][Epoch 10][best metric: 0.8911764738606471]
[2022/04/04 17:52:30] root INFO: Already save model in ./output/ResNet50_vd/epoch_10
[2022/04/04 17:52:31] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:33] root INFO: [Train][Epoch 11/20][Iter: 0/8]lr: 0.00923, CELoss: 0.42259, loss: 0.42259, batch_cost: 0.98574s, reader_cost: 0.57787, ips: 129.85227 images/sec, eta: 0:01:18
[2022/04/04 17:52:37] root INFO: [Train][Epoch 11/20][Avg]CELoss: 0.48286, loss: 0.48286
[2022/04/04 17:52:37] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:40] root INFO: [Train][Epoch 12/20][Iter: 0/8]lr: 0.00803, CELoss: 0.42716, loss: 0.42716, batch_cost: 1.01280s, reader_cost: 0.57595, ips: 126.38195 images/sec, eta: 0:01:12
[2022/04/04 17:52:44] root INFO: [Train][Epoch 12/20][Avg]CELoss: 0.35100, loss: 0.35100
[2022/04/04 17:52:44] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:47] root INFO: [Train][Epoch 13/20][Iter: 0/8]lr: 0.00674, CELoss: 0.27117, loss: 0.27117, batch_cost: 0.95948s, reader_cost: 0.56412, ips: 133.40572 images/sec, eta: 0:01:01
[2022/04/04 17:52:50] root INFO: [Train][Epoch 13/20][Avg]CELoss: 0.31605, loss: 0.31605
[2022/04/04 17:52:51] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:52:54] root INFO: [Train][Epoch 14/20][Iter: 0/8]lr: 0.00543, CELoss: 0.28394, loss: 0.28394, batch_cost: 1.13772s, reader_cost: 0.72433, ips: 112.50533 images/sec, eta: 0:01:03
[2022/04/04 17:52:57] root INFO: [Train][Epoch 14/20][Avg]CELoss: 0.26122, loss: 0.26122
[2022/04/04 17:52:58] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:53:01] root INFO: [Train][Epoch 15/20][Iter: 0/8]lr: 0.00416, CELoss: 0.16402, loss: 0.16402, batch_cost: 1.09271s, reader_cost: 0.68246, ips: 117.14036 images/sec, eta: 0:00:52
[2022/04/04 17:53:04] root INFO: [Train][Epoch 15/20][Avg]CELoss: 0.21305, loss: 0.21305
[2022/04/04 17:53:08] root INFO: [Eval][Epoch 15][Iter: 0/8]CELoss: 0.53277, loss: 0.53277, top1: 0.88281, top5: 0.94531, batch_cost: 3.47772s, reader_cost: 3.29045, ips: 36.80568 images/sec
[2022/04/04 17:53:10] root INFO: [Eval][Epoch 15][Avg]CELoss: 0.42379, loss: 0.42379, top1: 0.91863, top5: 0.98039
[2022/04/04 17:53:11] root INFO: Already save model in ./output/ResNet50_vd/best_model
[2022/04/04 17:53:11] root INFO: [Eval][Epoch 15][best metric: 0.9186274477079803]
[2022/04/04 17:53:11] root INFO: Already save model in ./output/ResNet50_vd/epoch_15
[2022/04/04 17:53:11] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:53:14] root INFO: [Train][Epoch 16/20][Iter: 0/8]lr: 0.00298, CELoss: 0.30324, loss: 0.30324, batch_cost: 0.98888s, reader_cost: 0.57842, ips: 129.43957 images/sec, eta: 0:00:39
[2022/04/04 17:53:18] root INFO: [Train][Epoch 16/20][Avg]CELoss: 0.22449, loss: 0.22449
[2022/04/04 17:53:18] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:53:21] root INFO: [Train][Epoch 17/20][Iter: 0/8]lr: 0.00195, CELoss: 0.19888, loss: 0.19888, batch_cost: 0.95664s, reader_cost: 0.56364, ips: 133.80189 images/sec, eta: 0:00:30
[2022/04/04 17:53:24] root INFO: [Train][Epoch 17/20][Avg]CELoss: 0.24154, loss: 0.24154
[2022/04/04 17:53:25] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:53:28] root INFO: [Train][Epoch 18/20][Iter: 0/8]lr: 0.00110, CELoss: 0.26926, loss: 0.26926, batch_cost: 1.14401s, reader_cost: 0.73260, ips: 111.88671 images/sec, eta: 0:00:27
[2022/04/04 17:53:31] root INFO: [Train][Epoch 18/20][Avg]CELoss: 0.23758, loss: 0.23758
[2022/04/04 17:53:32] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:53:35] root INFO: [Train][Epoch 19/20][Iter: 0/8]lr: 0.00048, CELoss: 0.31946, loss: 0.31946, batch_cost: 0.94231s, reader_cost: 0.54846, ips: 135.83640 images/sec, eta: 0:00:15
[2022/04/04 17:53:38] root INFO: [Train][Epoch 19/20][Avg]CELoss: 0.20997, loss: 0.20997
[2022/04/04 17:53:39] root INFO: Already save model in ./output/ResNet50_vd/latest
[2022/04/04 17:53:42] root INFO: [Train][Epoch 20/20][Iter: 0/8]lr: 0.00010, CELoss: 0.16348, loss: 0.16348, batch_cost: 1.05688s, reader_cost: 0.64513, ips: 121.11092 images/sec, eta: 0:00:08
[2022/04/04 17:53:45] root INFO: [Train][Epoch 20/20][Avg]CELoss: 0.20710, loss: 0.20710
[2022/04/04 17:53:48] root INFO: [Eval][Epoch 20][Iter: 0/8]CELoss: 0.51250, loss: 0.51250, top1: 0.89062, top5: 0.95312, batch_cost: 3.19902s, reader_cost: 2.99184, ips: 40.01228 images/sec
[2022/04/04 17:53:51] root INFO: [Eval][Epoch 20][Avg]CELoss: 0.40260, loss: 0.40260, top1: 0.92059, top5: 0.98235
[2022/04/04 17:53:52] root INFO: Already save model in ./output/ResNet50_vd/best_model
[2022/04/04 17:53:52] root INFO: [Eval][Epoch 20][best metric: 0.9205882329566806]
[2022/04/04 17:53:52] root INFO: Already save model in ./output/ResNet50_vd/epoch_20
[2022/04/04 17:53:52] root INFO: Already save model in ./output/ResNet50_vd/latest
Copy the code

The training log is as follows

[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/latestCopy the code

The visible log output is messy and not as clear as before, it is best to use VisualDL to see the training

4. Model export

! 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_inferCopy the code

[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
Copy the code

! ls ./deploy/models/class_ResNet50_vd_ImageNet_infer -laCopy the code

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.pdmodelCopy the code

5. OpenVINO prediction

Since AiStudio cannot use the latest version of OpenVINO, upload it after running locally

1. OpenVINO installation

Note here that you are using the latest version of OpenVINO, which is currently 2022.1.0

! pip install OpenVINOCopy the code

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: Requirement For 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)Copy the code

2.Import

Import the necessary OpenVINO libraries

# 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
Copy the code

3. The preprocessing

3.1 Generate flower classification dictionary

flowers_classes={}
for i in range(102):
    flowers_classes[str(i)]='flower_'+ str(i)
print(flowers_classes)
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{'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'}
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3.2 Preprocessing callback definition

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]])
            )
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3.3 Reading model

# 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)
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4. Call API for preprocessing

If the input data does not exactly conform to the model input tensor, additional operations/steps are required to transform the data into the format expected by the model. These operations are called “preprocessing”.
The preprocessing step is integrated into the execution diagram and executed on the selected device (CPU/GPU/VPU/ etc.) instead of always executing on the CPU. This will improve the utilization of the selected device.

Relevant API: docs. Openvino. Ai/latest/open…

# Images to be predicted
filename = "myflower.jpg"
test_image = cv2.imread(filename) 
test_image = np.expand_dims(test_image, 0) / 255
_, h, w, _ = test_image.shape
Adjust the model input image size
model.reshape({input_layer.any_name: PartialShape([1.3.224.224])})
ppp = PrePostProcessor(model)
Insert your tensor information:
# -input () provides input to the model
# - Data format "NHWC"
# - Set static model input dimensions
ppp.input().tensor() \
    .set_spatial_static_shape(h, w) \
    .set_layout(Layout("NHWC")) 
inputs = model.inputs
Let model have "NCHW" layout as input
ppp.input().model().set_layout(Layout("NCHW"))
# processing operations:
# - tensor RESIZE_LINEAR scaling Settings
# - Normalization of each channel
# - Divide each pixel data into appropriate scale values
ppp.input().preprocess() \
    .resize(ResizeAlgorithm.RESIZE_LINEAR, 224.224) \
    .mean([0.485.0.456.0.406]) \
    .scale([0.229.0.224.0.225])
# set output tensor information:
# - Tensor precision set to 'f32'
ppp.output().tensor().set_element_type(Type.f32)
# Apply preprocessing to modify the original 'model'
model = ppp.build()
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5.

With “AUTO” as the device name, entrust OpenVINO to select the device. Automatic device plugins internally identify and select devices from Intel between CPU and GPU depending on device features and model (e.g., precision). It then assigns the inference request to the best device.

AUTO immediately starts reasoning on the CPU and then transparently transfers to the GPU(or VPU) when it is ready, greatly reducing the time for the first reasoning.

# Check available devices
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}")

Load the model into the device selected by AUTO from the list of available devices
compiled_model = ie.compile_model(model=model, device_name="AUTO")
Create a request queue
infer_queue = AsyncInferQueue(compiled_model)
infer_queue.set_callback(callback)
start = time.time()
# Start predicting
infer_queue.start_async({input_layer.any_name: test_image}, 0)
infer_queue.wait_all()
Image(filename=filename) 
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CPU: Intel(R) Core(TM) i5-9400F CPU at 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: 0.02347Copy the code

6. Performance tips: latency and throughput

Throughput and latency are some of the most widely used measures of an application’s overall performance.

Delay is the time (ms) required to predict a single input
Throughput, processing time/number of inputs processed

OpenVINO performance hints are a new way to configure performance with portability in mind. Performance prompts will allow the device to configure itself, rather than mapping application needs to low-level performance Settings, and keep an associated application logic to configure each possible device separately.

Advanced techniques: docs. Openvino. Ai/latest/open…

6.1 Delay Calculation

The application’s performance Settings can be adjusted through configuration to allow the device to adjust for better delay-oriented performance.

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}")
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Latency: 0.018686771392822266 Class name: flower_76, probability: 0.40075 0.15170 class name: flower_91, probability: 0.03979 Class name: flower_12, probability: 0.03356 Class name: Flower_17, probability: 0.02347 FPS: 50.20953840260486Copy the code

6.2 Throughput Calculation

You can use configuration to set the performance Settings for your application and let the device adjust for better throughput performance.

# 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}")
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Latency: 0.04830741882324219 Class name: flower_76, probability: 0.40075 0.15170 class name: flower_91, probability: 0.03979 Class name: flower_12, probability: 0.03356 Class name: Flower_17, probability: 0.02347 FPS: 57.274455164002134Copy the code

! benchmark_app -m $model_path -data_shape [1.3.224.224] -hint "latency"
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[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 / 2020/1 [INFO] Device INFO CPU Openvino_intel_CPU_plugin version 2022.1 Build................... 2022.1.0-7019-CDB9BEC7210 - Releases / 2020/1 [Step 3/11] Setting Device Configuration [Step 4/11] Reading Network Files [ INFO] Read model took 144.60ms [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 , [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] Shrimp 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 FPSCopy the code

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Deployment Prediction of PaddlePaddle Flower Classification Model based on OpenVINO

One, flower recognition

1. Introduction to the data set

2. PaddleClas profile

3.OpenVINO 2022.1 Deployment support

4.OpenVINO 2022.1 Workflow

PaddleClas

3. Model training

1. Modify imagenet_dataset. Py

2. Modify the configuration file

3. Configuration description

3.1 Global Configuration (Global)

3.2 structure (Arch)

3.3 Loss Function (Loss)

3.4 Optimizer

4. Training

4. Model export

5. OpenVINO prediction

1. OpenVINO installation

2.Import

3. The preprocessing

3.1 Generate flower classification dictionary

3.2 Preprocessing callback definition

3.3 Reading model

4. Call API for preprocessing

5.

6. Performance tips: latency and throughput

6.1 Delay Calculation

6.2 Throughput Calculation

Deployment Prediction of PaddlePaddle Flower Classification Model based on OpenVINO

One, flower recognition

1. Introduction to the data set

2. PaddleClas profile

3.OpenVINO 2022.1 Deployment support

4.OpenVINO 2022.1 Workflow

PaddleClas

3. Model training

1. Modify imagenet_dataset. Py

2. Modify the configuration file

3. Configuration description

3.1 Global Configuration (Global)

3.2 structure (Arch)

3.3 Loss Function (Loss)

3.4 Optimizer

4. Training

4. Model export

5. OpenVINO prediction

1. OpenVINO installation

2.Import

3. The preprocessing

3.1 Generate flower classification dictionary

3.2 Preprocessing callback definition

3.3 Reading model

4. Call API for preprocessing

5.

6. Performance tips: latency and throughput

6.1 Delay Calculation

6.2 Throughput Calculation

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