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1fe211ffe5
* fix pipeline builder when model is not supported * fix ci & skip --------- Co-authored-by: suluyan.sly@alibaba-inc.com <suluyan.sly@alibaba-inc.com>
84 lines
3.7 KiB
Python
84 lines
3.7 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 unittest
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import json
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from modelscope.metainfo import Metrics, Trainers
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from modelscope.msdatasets import MsDataset
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from modelscope.trainers import build_trainer
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from modelscope.utils.constant import ModelFile
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from modelscope.utils.test_utils import test_level
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class TestClipTrainer(unittest.TestCase):
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def setUp(self) -> None:
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self.finetune_cfg = \
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{'framework': 'pytorch',
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'task': 'multi-modal-embedding',
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'pipeline': {'type': 'multi-modal-embedding'},
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'pretrained_model': {'model_name': 'damo/multi-modal_clip-vit-base-patch16_zh'},
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'dataset': {'column_map': {'img': 'image', 'text': 'query'}},
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'train': {'work_dir': './workspace/ckpts/clip',
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# 'launcher': 'pytorch',
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'max_epochs': 1,
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'use_fp16': True,
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'dataloader': {'batch_size_per_gpu': 8,
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'workers_per_gpu': 0,
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'shuffle': True,
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'drop_last': True},
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'lr_scheduler': {'name': 'cosine',
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'warmup_proportion': 0.01},
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'lr_scheduler_hook': {'type': 'LrSchedulerHook', 'by_epoch': False},
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'optimizer': {'type': 'AdamW'},
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'optimizer_hparams': {'lr': 5e-05, 'weight_decay': 0.01},
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'optimizer_hook': {'type': 'TorchAMPOptimizerHook',
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'cumulative_iters': 1,
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'loss_keys': 'loss'},
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'loss_cfg': {'aggregate': True},
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'hooks': [{'type': 'BestCkptSaverHook',
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'metric_key': 'inbatch_t2i_recall_at_1',
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'interval': 100},
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{'type': 'TextLoggerHook', 'interval': 1},
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{'type': 'IterTimerHook'},
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{'type': 'EvaluationHook', 'by_epoch': True, 'interval': 1},
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{'type': 'ClipClampLogitScaleHook'}]},
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'evaluation': {'dataloader': {'batch_size_per_gpu': 8,
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'workers_per_gpu': 0,
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'shuffle': True,
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'drop_last': True},
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'metrics': [{'type': 'inbatch_recall'}]},
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'preprocessor': []}
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@unittest.skip
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def test_trainer_std(self):
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WORKSPACE = './workspace/ckpts/clip'
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os.makedirs(WORKSPACE, exist_ok=True)
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config_file = os.path.join(WORKSPACE, ModelFile.CONFIGURATION)
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with open(config_file, 'w') as writer:
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json.dump(self.finetune_cfg, writer)
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pretrained_model = 'damo/multi-modal_clip-vit-base-patch16_zh'
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args = dict(
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model=pretrained_model,
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work_dir=WORKSPACE,
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train_dataset=MsDataset.load(
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'muge', namespace='modelscope', split='train[:200]'),
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eval_dataset=MsDataset.load(
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'muge', namespace='modelscope', split='validation[:100]'),
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metrics=[Metrics.inbatch_recall],
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cfg_file=config_file)
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trainer = build_trainer(
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name=Trainers.clip_multi_modal_embedding, default_args=args)
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trainer.train()
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self.assertIn(ModelFile.TORCH_MODEL_BIN_FILE,
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os.listdir(os.path.join(WORKSPACE, 'output')))
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shutil.rmtree(WORKSPACE)
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if __name__ == '__main__':
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unittest.main()
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