推理处理样例（ResNet-50）

代码演示

import numpy as np
from mindx.sdk import base
from mindx.sdk.base import Image, Model, ImageProcessor, Size

# 资源初始化
print('==========资源初始化=========')
base.mx_init()
# 设备Id
device_id = 0

# 图像解码
# 初始化ImageProcessor对象
imageProcessor = ImageProcessor(device_id)
image_path = "data/test_dog.jpg"
# 读取图片路径进行解码，解码格式为nv12（YUV_SP_420）
decoded_image = imageProcessor.decode(image_path, base.nv12)
print('decoded_image width:  {}'.format(decoded_image.original_width))
print('decoded_image height:  {}'.format(decoded_image.original_height))
print('==========图片解码完成=========')

# 图像缩放
# 缩放尺寸
size_para = Size(224, 224)
# 读取将解码后的Image类按尺寸进行缩放，缩放方式为华为自研的高阶滤波算法（huaweiu_high_order_filter）
resized_image = imageProcessor.resize(decoded_image, size_para, base.huaweiu_high_order_filter)
print('resized_image width:  ', resized_image.original_width)
print('resized_image height:  ', resized_image.original_height)
print('==========图片缩放完成=========')

# 模型推理
# Image类转为Tensor类并传入列表
input_tensors = [resized_image.to_tensor()]
# 模型路径
model_path = "./model/resnet50_batchsize_1.om"
# 初始化Model类
# 也可使用model = base.model(modelPath=model_path, deviceId=device_id)
model = Model(modelPath=model_path, deviceId=device_id)
# 执行推理
outputs = model.infer(input_tensors)
print('==========模型推理完成=========')

# 后处理
# 获取推理结果置信度tensor
confidence_tensor = outputs[0]
# 将tensor数据转移到Host侧
confidence_tensor.to_host()
# 将Tensor类转为numpy array类型
confidence_array = np.array(confidence_tensor)
# 获取最大置信度序号和置信度
max_confidence_index = confidence_array.argmax(axis=1)[0]
max_confidence_value = confidence_array[0][max_confidence_index]
print('max_confidence_index:', max_confidence_index)
print('max_confidence_value:', max_confidence_value)
# 分类标签文件路径
label_path = "./model/resnet50_clsidx_to_labels.names"
# 读取分类标签文件并查询分类结果
with open(label_path, 'r', encoding='utf-8') as f:
    file = f.read()
label_list = file.split('\n')[1:-1]
infer_result = label_list[max_confidence_index]
print('classification result:{}'.format(infer_result))