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https://github.com/modelscope/modelscope.git
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e288cf076e
* using get_model to validate hub path * support reading pipeline info from configuration file * add metainfo const * update model type and pipeline type and fix UT * relax requimrent for protobuf * skip two dataset tests due to temporal failure Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9118154
116 lines
4.4 KiB
Python
116 lines
4.4 KiB
Python
import unittest
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import datasets as hfdata
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from modelscope.models import Model
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from modelscope.preprocessors import SequenceClassificationPreprocessor
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from modelscope.preprocessors.base import Preprocessor
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from modelscope.pydatasets import PyDataset
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from modelscope.utils.constant import Hubs
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from modelscope.utils.test_utils import require_tf, require_torch, test_level
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class ImgPreprocessor(Preprocessor):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.path_field = kwargs.pop('image_path', 'image_path')
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self.width = kwargs.pop('width', 'width')
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self.height = kwargs.pop('height', 'width')
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def __call__(self, data):
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import cv2
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image_path = data.get(self.path_field)
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if not image_path:
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return None
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img = cv2.imread(image_path)
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return {
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'image':
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cv2.resize(img,
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(data.get(self.height, 128), data.get(self.width, 128)))
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}
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class PyDatasetTest(unittest.TestCase):
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@unittest.skipUnless(test_level() >= 2,
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'skip test due to dataset api problem')
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def test_ds_basic(self):
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ms_ds_full = PyDataset.load('squad')
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ms_ds_full_hf = hfdata.load_dataset('squad')
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ms_ds_train = PyDataset.load('squad', split='train')
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ms_ds_train_hf = hfdata.load_dataset('squad', split='train')
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ms_image_train = PyDataset.from_hf_dataset(
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hfdata.load_dataset('beans', split='train'))
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self.assertEqual(ms_ds_full['train'][0], ms_ds_full_hf['train'][0])
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self.assertEqual(ms_ds_full['validation'][0],
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ms_ds_full_hf['validation'][0])
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self.assertEqual(ms_ds_train[0], ms_ds_train_hf[0])
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print(next(iter(ms_ds_full['train'])))
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print(next(iter(ms_ds_train)))
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print(next(iter(ms_image_train)))
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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@require_torch
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def test_to_torch_dataset_text(self):
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model_id = 'damo/bert-base-sst2'
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nlp_model = Model.from_pretrained(model_id)
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preprocessor = SequenceClassificationPreprocessor(
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nlp_model.model_dir,
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first_sequence='context',
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second_sequence=None)
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ms_ds_train = PyDataset.load('squad', split='train')
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pt_dataset = ms_ds_train.to_torch_dataset(preprocessors=preprocessor)
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import torch
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dataloader = torch.utils.data.DataLoader(pt_dataset, batch_size=5)
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print(next(iter(dataloader)))
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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@require_tf
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def test_to_tf_dataset_text(self):
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import tensorflow as tf
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tf.compat.v1.enable_eager_execution()
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model_id = 'damo/bert-base-sst2'
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nlp_model = Model.from_pretrained(model_id)
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preprocessor = SequenceClassificationPreprocessor(
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nlp_model.model_dir,
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first_sequence='context',
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second_sequence=None)
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ms_ds_train = PyDataset.load('squad', split='train')
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tf_dataset = ms_ds_train.to_tf_dataset(
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batch_size=5,
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shuffle=True,
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preprocessors=preprocessor,
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drop_remainder=True)
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print(next(iter(tf_dataset)))
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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@require_torch
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def test_to_torch_dataset_img(self):
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ms_image_train = PyDataset.from_hf_dataset(
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hfdata.load_dataset('beans', split='train'))
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pt_dataset = ms_image_train.to_torch_dataset(
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preprocessors=ImgPreprocessor(
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image_path='image_file_path', label='labels'))
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import torch
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dataloader = torch.utils.data.DataLoader(pt_dataset, batch_size=5)
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print(next(iter(dataloader)))
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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@require_tf
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def test_to_tf_dataset_img(self):
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import tensorflow as tf
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tf.compat.v1.enable_eager_execution()
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ms_image_train = PyDataset.load('beans', split='train')
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tf_dataset = ms_image_train.to_tf_dataset(
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batch_size=5,
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shuffle=True,
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preprocessors=ImgPreprocessor(image_path='image_file_path'),
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drop_remainder=True,
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label_cols='labels')
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print(next(iter(tf_dataset)))
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if __name__ == '__main__':
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unittest.main()
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