Sample Overview

Sample Obtaining

Visit the Ascend samples repository on Gitee and download the sample package that matches your CANN version in use. For the version mapping, see "Release Notes" in the README file. Find the "resnet50_imagenet_classification" sample in the python/level2_simple_inference/1_classification/resnet50_imagenet_classification directory.

Function Description

This sample shows how to classify images based on the Caffe ResNet-50 network (single input with batch size = 1).

Convert the model file of the Caffe ResNet-50 network into an offline model (.om file) that adapts to Ascend AI Processors. In the sample, load the .om file, infer two .jpg images synchronously, obtain the inference results, process the inference results, and outputs the class indexes with the top 5 confidence values.

Main APIs

Table 1 shows the main APIs.

**Table 1** Main APIs
Function	ACL Module	ACL Interface Function	Description
Resource initialization	Initialization	acl.init	Initializes the ACL configuration.
	Device management	acl.rt.set_device	Specifies the device for computation.
	Context management	acl.rt.create_context	Creates a context.
Model initialization	Model loading and execution	acl.mdl.load_from_file	Loads the model from the .om file to the device.
	Data types and operation APIs	acl.mdl.create_desc	Creates data for describing the model.
	Data types and operation APIs	acl.mdl.get_desc	Obtains data for describing the model.
Model inference	Model loading and execution	acl.mdl.execute	Performs synchronous model inference.
Data exchange	Memory management	acl.rt.memcpy	Sends data from the host to the device or from the device to the host.
	Memory management	acl.rt.malloc	Allocates device memory.
	Memory management	acl.rt.malloc_host	Allocates memory on the host.
Common module	--	acl.util.ptr_to_numpy	Converts void* data to NumPy data.
Common module	--	acl.util.numpy_to_ptr	Converts NumPy data to void* data.
Data postprocessing	Data types and operation APIs	acl.mdl.get_dataset_buffer	Obtains dataset information.
Data postprocessing	Data types and operation APIs	acl.mdl.get_dataset_num_buffers	Obtains dataset information.
Allocation destruction	Memory management	acl.rt.free	Frees device memory.
	Memory management	acl.rt.free_host	Frees the memory on the host.
	Model loading and execution	acl.mdl.unload	Unloads a model.
	Context management	acl.rt.destroy_context	Destroys a context.
	Device management	acl.rt.reset_device	Resets the current device and reclaims the resources on the device.
	Deinitialization	acl.finalize	Deinitializes ACL.

Synchronous Model Inference Process

Figure 1 shows the synchronous model inference process.

Figure 1 Synchronous model inference process

Directory Structure

The directory structure is as follows:

resnet50_imagenet_classification
├──src
│ ├── acl_net.py // Running file
│ └── constant.py // Constant definition
├── data
│ ├── dog1_1024_683.jpg // Test image data
│ └── dog2_1024_683.jpg // Test image data
├── caffe_model
│ ├── resnet50.caffemodel // ResNet-50 model
│ └── resnet50.prototxt // ResNet-50 network file
└── model
  └── resnet50.om // Model file generated after conversion

Parent topic: Image Classification with Caffe ResNet-50 (Synchronous Inference)