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doc: update stdc_step_by_step: some typos and minor fixes

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chingning.chen 2022-04-15 14:34:53 +08:00
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@ -44,7 +44,7 @@ git clone https://github.com/kneron/kneron-mmsegmentation.git
cd kneron-mmsegmentation cd kneron-mmsegmentation
``` ```
### Step 1-2-4: Install Required Python Packages for Building and Installing kneron-mmsegmentation ### Step 1-2-4: Install Required Python Libraries for Building and Installing kneron-mmsegmentation
```shell ```shell
pip install -r requirements_kneron.txt pip install -r requirements_kneron.txt
@ -83,7 +83,7 @@ ln -s $(realpath path/to/your/cityscapes) kneron-mmsegmentation/data
# Replace all "path/to/your" above with where you want to put the dataset! # Replace all "path/to/your" above with where you want to put the dataset!
``` ```
Then, we need *cityscapesScripts* to preprocess the CityScapes dataset. If you completely followed our [Step 1-2-4](#step-1-2-4-install-required-python-packages-for-building-and-installing-kneron-mmsegmentation), you should have python package *cityscapesScripts* installed (if no, execute `pip install cityscapesScripts` command). Then, we need *cityscapesScripts* to preprocess the CityScapes dataset. If you completely followed our [Step 1-2-4](#step-1-2-4-install-required-python-libraries-for-building-and-installing-kneron-mmsegmentation), you should have python library *cityscapesScripts* installed (if no, execute `pip install cityscapesScripts` command).
```shell ```shell
# Replace "path/to/your" with where you want to put the dataset! # Replace "path/to/your" with where you want to put the dataset!
@ -156,7 +156,7 @@ cd kneron-mmsegmentation
python tools/train.py configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py python tools/train.py configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py
``` ```
And kneron-mmsegmentation will generate `work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes` folder and save the configuration file and all checkpoints there. kneron-mmsegmentation will generate `work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes` folder and save the configuration file and all checkpoints there.
# Step 3: Test Trained Model # Step 3: Test Trained Model
`tools/test.py` is a script that generates inference results from test set with our pytorch model and evaluates the results to see if our pytorch model is well trained (if `--eval` argument is given). Note that it's always good to evluate our pytorch model before deploying it. `tools/test.py` is a script that generates inference results from test set with our pytorch model and evaluates the results to see if our pytorch model is well trained (if `--eval` argument is given). Note that it's always good to evluate our pytorch model before deploying it.
@ -241,19 +241,19 @@ Note that the ONNX results may differ from the PyTorch results due to some imple
# Step 5: Convert ONNX File to [NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) Model for Kneron Platform # Step 5: Convert ONNX File to [NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) Model for Kneron Platform
### Step 5-1: Install Kneron toolchain docker: ## Step 5-1: Install Kneron toolchain docker:
* check [document](http://doc.kneron.com/docs/#toolchain/manual/#1-installation) * Check [Kneron Toolchain Installation Document](http://doc.kneron.com/docs/#toolchain/manual/#1-installation)
### Step 5-2: Mout Kneron toolchain docker ## Step 5-2: Mount Kneron toolchain docker
* Mount a folder (e.g. '/mnt/hgfs/Competition') to toolchain docker container as `/data1`. The converted ONNX in Step 3 should be put here. All the toolchain operation should happen in this folder. * Mount a folder (e.g. '/mnt/hgfs/Competition') to toolchain docker container as `/data1`. The converted ONNX in Step 3 should be put here. All the toolchain operation should happen in this folder.
``` ```
sudo docker run --rm -it -v /mnt/hgfs/Competition:/data1 kneron/toolchain:latest sudo docker run --rm -it -v /mnt/hgfs/Competition:/data1 kneron/toolchain:latest
``` ```
### Step 5-3: Import KTC and required lib in python shell ## Step 5-3: Import KTC and the required libraries in python
* Here we demonstrate how to go through all Kneron Toolchain (KTC) flow through Python API:
```python ```python
import ktc import ktc
import numpy as np import numpy as np
@ -262,7 +262,8 @@ import onnx
from PIL import Image from PIL import Image
``` ```
### Step 5-4: Optimize the onnx model ## Step 5-4: Optimize the onnx model
```python ```python
onnx_path = '/data1/latest.onnx' onnx_path = '/data1/latest.onnx'
m = onnx.load(onnx_path) m = onnx.load(onnx_path)
@ -270,7 +271,7 @@ m = ktc.onnx_optimizer.onnx2onnx_flow(m)
onnx.save(m,'latest.opt.onnx') onnx.save(m,'latest.opt.onnx')
``` ```
### Step 5-5: Configure and load data necessary for ktc, and check if onnx is ok for toolchain ## Step 5-5: Configure and load data needed for ktc, and check if onnx is ok for toolchain
```python ```python
# npu (only) performance simulation # npu (only) performance simulation
km = ktc.ModelConfig((&)model_id_on_public_field, "0001", "720", onnx_model=m) km = ktc.ModelConfig((&)model_id_on_public_field, "0001", "720", onnx_model=m)
@ -278,13 +279,13 @@ eval_result = km.evaluate()
print("\nNpu performance evaluation result:\n" + str(eval_result)) print("\nNpu performance evaluation result:\n" + str(eval_result))
``` ```
### Step 5-6: quantize the onnx model ## Step 5-6: Quantize the onnx model
We [sampled 3 images from Cityscapes dataset](https://www.kneron.com/tw/support/education-center/?folder=OpenMMLab%20Kneron%20Edition/misc/&download=41) (3 images) as quantization data. To test our quantized model: We [sampled 3 images from Cityscapes dataset](https://www.kneron.com/tw/support/education-center/?folder=OpenMMLab%20Kneron%20Edition/misc/&download=41) (3 images) as quantization data. To test our quantized model:
1. Download the zip file 1. Download the [zip file](https://www.kneron.com/tw/support/education-center/?folder=OpenMMLab%20Kneron%20Edition/misc/&download=41)
2. Extract the zip file as a folder named `cityscapes_minitest` 2. Extract the zip file as a folder named `cityscapes_minitest`
3. Put the `cityscapes_minitest` into docker mounted folder (the path in docker container should be `/data1/cityscapes_minitest`) 3. Put the `cityscapes_minitest` into docker mounted folder (the path in docker container should be `/data1/cityscapes_minitest`)
The following script will do some preprocess(should be the same as training code) on our quantization data, and put it in a list: The following script will preprocess (should be the same as training code) our quantization data, and put it in a list:
```python ```python
import os import os
@ -303,7 +304,7 @@ for (dirpath, dirnames, filenames) in walk("/data1/cityscapes_minitest"):
img_list.append(img_data) img_list.append(img_data)
``` ```
Then perform quantization. The BIE model will be generated at `/data1/output.bie`. Then perform quantization. The generated BIE model will put generated at `/data1/output.bie`.
```python ```python
# fixed-point analysis # fixed-point analysis
@ -311,7 +312,7 @@ bie_model_path = km.analysis({"input": img_list})
print("\nFixed-point analysis done. Save bie model to '" + str(bie_model_path) + "'") print("\nFixed-point analysis done. Save bie model to '" + str(bie_model_path) + "'")
``` ```
### Step 5-7: Compile ## Step 5-7: Compile
The final step is compile the BIE model into an NEF model. The final step is compile the BIE model into an NEF model.
```python ```python
@ -343,23 +344,23 @@ Recommend you read [Kneron PLUS official document](http://doc.kneron.com/docs/#p
### Step 7-2: Download STDC example code ### Step 7-2: Download STDC example code
* Go to [Kneron education center](https://www.kneron.com/tw/support/education-center/) * Go to [Kneron education center](https://www.kneron.com/tw/support/education-center/)
* Scroll down to OpenMMLab Kneron Edition table * Scroll down to **OpenMMLab Kneron Edition** table
* Select kneron-mmsegmentation * Select **kneron-mmsegmentation**
* Select STDC * Select **STDC**
* Download stdc_plus_demo.zip * Download **stdc_plus_demo.zip**
* unzip downloaded `stdc_plus_demo` * unzip downloaded **stdc_plus_demo**
### Step 7-3: Test enviroment is ready (require [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1)) ### Step 7-3: Test enviroment is ready (require [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1))
In `stdc_plus_demo`, we provide a stdc example model and image for quick test. In `stdc_plus_demo`, we provide a STDC-Seg example model and image for quick test.
* Plug in [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1) into your computer USB port * Plug in [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1) into your computer USB port
* Go to the stdc_plus_demo folder * Go to the stdc_plus_demo folder
```bash ```bash
cd /PATH/TO/stdc_plus_demo cd /PATH/TO/stdc_plus_demo
``` ```
* Install required library * Install required python libraries
```bash ```bash
pip insall -r requirements.txt pip install -r requirements.txt
``` ```
* Run example on [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1) * Run example on [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1)
@ -368,7 +369,7 @@ python KL720DemoGenericInferenceSTDC_BypassHwPreProc.py -nef ./example_stdc_720.
``` ```
Then you can see the inference result is saved as output_000000000641.jpg in the same folder. Then you can see the inference result is saved as output_000000000641.jpg in the same folder.
And the expected result of the command above will be something similar to the following text: The expected result of the command above will be something similar to the following text:
```plain ```plain
... ...
[Connect Device] [Connect Device]