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e6320f29d3
1. Exporting: Support text-classification of bert and tensorflow2.0 models, test cases have been added.
2. Downloading of preprocessor.from_pretrained will ignores some large files which not needed by extension file name.
3. Move sentence-piece-preprocessor to the subclass of text-generation-preprocessor and keep the original name for compatibility.
4. Remove some useless codes in nlp-trainer and trainer.
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11206922
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
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from tensorflow.keras.preprocessing import image
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from modelscope.exporters import TfModelExporter
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from modelscope.utils.regress_test_utils import compare_arguments_nested
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from modelscope.utils.test_utils import test_level
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class TestExportTfModel(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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super().tearDown()
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_export_resnet50(self):
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img_path = 'data/test/images/auto_demo.jpg'
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img = image.load_img(img_path, target_size=(224, 224))
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x = image.img_to_array(img)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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x_t = tf.convert_to_tensor(x)
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model = ResNet50(weights='imagenet')
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def call_func(inputs):
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return [model.predict(list(inputs.values())[0])]
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output_files = TfModelExporter().export_onnx(
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model=model,
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dummy_inputs={'input': x_t},
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call_func=call_func,
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output_dir=self.tmp_dir)
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print(output_files)
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if __name__ == '__main__':
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unittest.main()
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