mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
1135d33dd7
* 第一次提交 * 补充一处漏翻译 * deleted: docs/en/quantize.md * Update one translation * Update en version * Update one translation in code * Standardize one writing * Standardize one writing * Update some en version * Fix a grammer problem * Update en version for api/vision result * Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop * Checkout the link in README in vision_results/ to the en documents * Modify a title * Add link to serving/docs/ * Finish translation of demo.md * Update english version of serving/docs/ * Update title of readme * Update some links * Modify a title * Update some links * Update en version of java android README * Modify some titles * Modify some titles * Modify some titles * modify article to document * update some english version of documents in examples * Add english version of documents in examples/visions * Sync to current branch * Add english version of documents in examples
55 lines
1.7 KiB
Markdown
55 lines
1.7 KiB
Markdown
English | [中文](../../../cn/quick_start/runtime/python.md)
|
|
# Python Inference
|
|
|
|
Please check out the FastDeploy is already installed in your environment. You can refer to [FastDeploy Installation](../../build_and_install/) to install the pre-compiled FastDeploy, or customize your installation.
|
|
|
|
This document shows an inference sample on the CPU using the PaddleClas classification model MobileNetV2 as an example.
|
|
|
|
## 1. Obtaining the model
|
|
|
|
``` python
|
|
import fastdeploy as fd
|
|
|
|
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
|
|
fd.download_and_decompress(model_url, path=".")
|
|
```
|
|
|
|
## 2. Backend Configuration
|
|
|
|
- For more examples, you can refer to [examples/runtime](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/runtime).
|
|
|
|
``` python
|
|
option = fd.RuntimeOption()
|
|
|
|
option.set_model_path("mobilenetv2/inference.pdmodel",
|
|
"mobilenetv2/inference.pdiparams")
|
|
|
|
# **** CPU Configuration ****
|
|
option.use_cpu()
|
|
option.use_ort_backend()
|
|
option.set_cpu_thread_num(12)
|
|
|
|
# Initialise runtime
|
|
runtime = fd.Runtime(option)
|
|
|
|
# Get model input name
|
|
input_name = runtime.get_input_info(0).name
|
|
|
|
# Constructing random data for inference
|
|
results = runtime.infer({
|
|
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
|
|
})
|
|
|
|
print(results[0].shape)
|
|
```
|
|
When loading is complete, you will get the following output information indicating the initialized backend and the hardware devices.
|
|
```
|
|
[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.
|
|
```
|
|
|
|
## Other Documents
|
|
|
|
- [Runtime demos on different backends](../../../../examples/runtime/README.md)
|
|
- [Switching hardware and backend for model inference](../../faq/how_to_change_backend.md)
|
|
|