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105 lines
3.2 KiB
105 lines
3.2 KiB
from towhee import ops
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from timm_image import TimmImage
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import torch
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import numpy
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import onnx
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import onnxruntime
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import os
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from pathlib import Path
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import logging
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import platform
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import psutil
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# models = TimmImage.supported_model_names()[:2]
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models = ['resnet50']
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atol = 1e-3
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log_path = 'timm_onnx.log'
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f = open('onnx.csv', 'w+')
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f.write('model,load_op,save_onnx,check_onnx,run_onnx,accuracy\n')
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logger = logging.getLogger('timm_onnx')
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logger.setLevel(logging.DEBUG)
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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fh = logging.FileHandler(log_path)
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fh.setLevel(logging.DEBUG)
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fh.setFormatter(formatter)
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logger.addHandler(fh)
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ch = logging.StreamHandler()
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ch.setLevel(logging.ERROR)
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ch.setFormatter(formatter)
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logger.addHandler(ch)
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logger.debug(f'machine: {platform.platform()}-{platform.processor()}')
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logger.debug(f'free/available/total mem: {round(psutil.virtual_memory().free / (1024.0 ** 3))}'
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f'/{round(psutil.virtual_memory().available / (1024.0 ** 3))}'
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f'/{round(psutil.virtual_memory().total / (1024.0 ** 3))} GB')
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logger.debug(f'cpu: {psutil.cpu_count()}')
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status = None
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for name in models:
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logger.info(f'***{name}***')
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saved_name = name.replace('/', '-')
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onnx_path = f'saved/onnx/{saved_name}.onnx'
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try:
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op = ops.image_embedding.timm(model_name=name, device='cpu').get_op()
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data = torch.rand((1,) + op.config['input_size'])
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except Exception as e:
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print(f'***Please re-download model {name}.***')
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logger.error(f'Fail to call model: {e}')
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continue
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if status:
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f.write(','.join(status) + '\n')
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status = [name] + ['fail'] * 5
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try:
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out0 = op(data.cpu().detach().numpy().squeeze(0))
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assert out0.shape
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out1 = op.model(data).detach().numpy()
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logger.info('OP LOADED.')
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status[1] = 'success'
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except Exception as e:
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logger.error(f'FAIL TO LOAD OP: {e}')
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continue
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try:
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op.save_model(format='onnx')
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logger.info('ONNX SAVED.')
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status[2] = 'success'
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except Exception as e:
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logger.error(f'FAIL TO SAVE ONNX: {e}')
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continue
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try:
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try:
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onnx_model = onnx.load(onnx_path)
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onnx.checker.check_model(onnx_model)
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except Exception:
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saved_onnx = onnx.load(onnx_path, load_external_data=False)
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onnx.checker.check_model(saved_onnx)
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logger.info('ONNX CHECKED.')
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status[3] = 'success'
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except Exception as e:
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logger.error(f'FAIL TO CHECK ONNX: {e}')
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pass
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try:
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sess = onnxruntime.InferenceSession(onnx_path,
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providers=onnxruntime.get_available_providers())
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out2 = sess.run(None, input_feed={'input_0': data.detach().numpy()})
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logger.info('ONNX WORKED.')
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status[4] = 'success'
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if numpy.allclose(out1, out2, atol=atol):
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logger.info('Check accuracy: OK')
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status[5] = 'success'
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else:
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logger.info(f'Check accuracy: atol is larger than {atol}.')
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except Exception as e:
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logger.error(f'FAIL TO RUN ONNX: {e}')
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continue
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if status:
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f.write(','.join(status) + '\n')
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print('Finished.')
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