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Add table-transformer-detection ONNXRT example (#1314)
Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
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examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/export.sh
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examples/onnxrt/object_detection/table_transformer/quantization/ptq_static/prepare_model.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,100 @@ | ||
| import argparse | ||
| import os | ||
| import subprocess | ||
| import sys | ||
| from urllib import request | ||
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| MODEL_URLS = {"structure_detr": "https://huggingface.co/bsmock/tatr-pubtables1m-v1.0/resolve/main/pubtables1m_structure_detr_r18.pth", | ||
| "detection_detr": "https://huggingface.co/bsmock/tatr-pubtables1m-v1.0/resolve/main/pubtables1m_detection_detr_r18.pth"} | ||
| MAX_TIMES_RETRY_DOWNLOAD = 5 | ||
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| def parse_arguments(): | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument("--input_model", | ||
| type=str, | ||
| required=False, | ||
| choices=["structure_detr", "detection_detr"], | ||
| default="structure_detr") | ||
| parser.add_argument("--output_model", type=str, required=True) | ||
| parser.add_argument("--dataset_location", type=str, required=True) | ||
| return parser.parse_args() | ||
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| def progressbar(cur, total=100): | ||
| percent = '{:.2%}'.format(cur / total) | ||
| sys.stdout.write("\r[%-100s] %s" % ('#' * int(cur), percent)) | ||
| sys.stdout.flush() | ||
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| def schedule(blocknum, blocksize, totalsize): | ||
| if totalsize == 0: | ||
| percent = 0 | ||
| else: | ||
| percent = min(1.0, blocknum * blocksize / totalsize) * 100 | ||
| progressbar(percent) | ||
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| def download_model(url, retry_times=5): | ||
| model_name = url.split("/")[-1] | ||
| if os.path.isfile(model_name): | ||
| print(f"{model_name} exists, skip download") | ||
| return True | ||
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| print("download model...") | ||
| retries = 0 | ||
| while retries < retry_times: | ||
| try: | ||
| request.urlretrieve(url, model_name, schedule) | ||
| break | ||
| except KeyboardInterrupt: | ||
| return False | ||
| except: | ||
| retries += 1 | ||
| print(f"Download failed{', Retry downloading...' if retries < retry_times else '!'}") | ||
| return retries < retry_times | ||
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| def export_model(input_model, output_model, dataset_location): | ||
| print("\nexport model...") | ||
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| if not os.path.exists("./table-transformer"): | ||
| subprocess.run("bash prepare.sh", shell=True) | ||
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| model_load_path = os.path.abspath(MODEL_URLS[input_model].split("/")[-1]) | ||
| output_model = os.path.join(os.path.dirname(model_load_path), output_model) | ||
| if input_model == "detection_detr": | ||
| data_root_dir = os.path.join(dataset_location, "PubTables-1M-Detection") | ||
| data_type = "detection" | ||
| config_file = "detection_config.json" | ||
| elif input_model == "structure_detr": | ||
| data_root_dir = os.path.join(dataset_location, "PubTables-1M-Structure") | ||
| data_type = "structure" | ||
| config_file = "structure_config.json" | ||
| table_words_dir = os.path.join(data_root_dir, "words") | ||
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| os.chdir("table-transformer/src") | ||
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| command = f"python main.py \ | ||
| --model_load_path {model_load_path} \ | ||
| --output_model {output_model} \ | ||
| --data_root_dir {data_root_dir} \ | ||
| --table_words_dir {table_words_dir} \ | ||
| --mode export \ | ||
| --data_type {data_type} \ | ||
| --device cpu \ | ||
| --config_file {config_file}" | ||
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| subprocess.run(command, shell=True) | ||
| assert os.path.exists(output_model), f"Export failed! {output_model} doesn't exist!" | ||
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| def prepare_model(input_model, output_model, dataset_location): | ||
| is_download_successful = download_model(MODEL_URLS[args.input_model], MAX_TIMES_RETRY_DOWNLOAD) | ||
| if is_download_successful: | ||
| export_model(input_model, output_model, dataset_location) | ||
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| if __name__ == "__main__": | ||
| args = parse_arguments() | ||
| prepare_model(args.input_model, args.output_model, args.dataset_location) |
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