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* rockchip: update dependencies and add script for model conversion * rockchip: update docs --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
import os
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import rknn
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import yaml
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from rknn.api import RKNN
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try:
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with open(rknn.__path__[0] + "/VERSION") as file:
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tk_version = file.read().strip()
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except FileNotFoundError:
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pass
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try:
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with open("/config/conv2rknn.yaml", "r") as config_file:
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configuration = yaml.safe_load(config_file)
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except FileNotFoundError:
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raise Exception("Please place a config.yaml file in /config/conv2rknn.yaml")
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if configuration["config"] != None:
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rknn_config = configuration["config"]
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else:
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rknn_config = {}
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if not os.path.isdir("/config/model_cache/rknn_cache/onnx"):
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raise Exception(
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"Place the onnx models you want to convert to rknn format in /config/model_cache/rknn_cache/onnx"
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)
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if "soc" not in configuration:
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try:
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with open("/proc/device-tree/compatible") as file:
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soc = file.read().split(",")[-1].strip("\x00")
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except FileNotFoundError:
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raise Exception("Make sure to run docker in privileged mode.")
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configuration["soc"] = [
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soc,
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]
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if "quantization" not in configuration:
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configuration["quantization"] = False
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if "output_name" not in configuration:
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configuration["output_name"] = "{{input_basename}}"
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for input_filename in os.listdir("/config/model_cache/rknn_cache/onnx"):
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for soc in configuration["soc"]:
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quant = "i8" if configuration["quantization"] else "fp16"
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input_path = "/config/model_cache/rknn_cache/onnx/" + input_filename
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input_basename = input_filename[: input_filename.rfind(".")]
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output_filename = (
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configuration["output_name"].format(
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quant=quant,
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input_basename=input_basename,
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soc=soc,
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tk_version=tk_version,
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)
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+ ".rknn"
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)
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output_path = "/config/model_cache/rknn_cache/" + output_filename
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rknn_config["target_platform"] = soc
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rknn = RKNN(verbose=True)
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rknn.config(**rknn_config)
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if rknn.load_onnx(model=input_path) != 0:
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raise Exception("Error loading model.")
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if (
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rknn.build(
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do_quantization=configuration["quantization"],
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dataset="/COCO/coco_subset_20.txt",
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)
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!= 0
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):
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raise Exception("Error building model.")
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if rknn.export_rknn(output_path) != 0:
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raise Exception("Error exporting rknn model.")
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