From 48e6d06e5eb59bada87bc89ce0f6136dee0f6be6 Mon Sep 17 00:00:00 2001 From: EricChunYi Date: Thu, 7 Apr 2022 15:58:59 +0800 Subject: [PATCH] doc: revise step-by-step --- docs_kneron/stdc_step_by_step.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs_kneron/stdc_step_by_step.md b/docs_kneron/stdc_step_by_step.md index d1fd596..ed9d1d7 100644 --- a/docs_kneron/stdc_step_by_step.md +++ b/docs_kneron/stdc_step_by_step.md @@ -279,7 +279,7 @@ print("\nNpu performance evaluation result:\n" + str(eval_result)) ``` ### Step 5-6: quantize the onnx model -We [sampled 3 images from Cityscapes dataset](?) (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=MMLab/MMSegmentationKN/&download=41) (3 images) as quantization data. To test our quantized model: 1. Download the zip file 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`) @@ -298,7 +298,7 @@ for (dirpath, dirnames, filenames) in walk("/data1/cityscapes_minitest"): image = Image.open(fullpath) image = image.convert("RGB") image = Image.fromarray(np.array(image)[...,::-1]) - img_data = np.array(image.resize((640, 640), Image.BILINEAR)) / 256 - 0.5 + img_data = np.array(image.resize((1024, 512), Image.BILINEAR)) / 256 - 0.5 print(fullpath) img_list.append(img_data) ```