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Update run.py with ann_search.milvus_client 

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
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Jael Gu 2 years ago
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88086ae2a8
commit
34b3d3f0b8
1 changed files with 14 additions and 9 deletions
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  1. 23
      benchmark/run.py

23
benchmark/run.py
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@ -104,13 +104,15 @@ if args.format == 'pytorch':
.text_embedding.transformers['text', 'emb'](model_name=model_name, device=device)
.runas_op['emb', 'emb'](lambda x: x[0])
.runas_op['gt', 'gt'](lambda y: str(y))
.ann_search.milvus['emb', 'milvus_res'](
uri=f'tcp://{host}:{port}/{collection_name}',
.ann_search.milvus_client['emb', 'milvus_res'](
host=host,
port=port,
collection_name=collection_name,
metric_type=metric_type,
limit=topk,
output_fields=['label']
)
.runas_op['milvus_res', 'preds'](lambda x: [y.label for y in x]).unstream()
.runas_op['milvus_res', 'preds'](lambda x: [y[-1] for y in x]).unstream()
.runas_op['preds', 'pred1'](lambda x: mode(x[:1]))
.runas_op['preds', 'pred5'](lambda x: mode(x[:5]))
.runas_op['preds', 'pred10'](lambda x: mode(x[:10]))
@ -178,13 +180,15 @@ elif args.format == 'onnx':
.run_onnx['text', 'emb']()
.runas_op['emb', 'emb'](lambda x: x[0])
.runas_op['gt', 'gt'](lambda y: str(y))
.ann_search.milvus['emb', 'milvus_res'](
uri=f'tcp://{host}:{port}/{collection_name}',
.ann_search.milvus_client['emb', 'milvus_res'](
host=host,
port=port,
collection_name=collection_name,
metric_type=metric_type,
limit=topk,
output_fields=['label']
)
.runas_op['milvus_res', 'preds'](lambda x: [y.label for y in x]).unstream()
.runas_op['milvus_res', 'preds'](lambda x: [y[-1] for y in x]).unstream()
.runas_op['preds', 'pred1'](lambda x: mode(x[:1]))
.runas_op['preds', 'pred5'](lambda x: mode(x[:5]))
.runas_op['preds', 'pred10'](lambda x: mode(x[:10]))
@ -198,7 +202,8 @@ elif args.format == 'onnx':
else:
raise AttributeError('Only support "pytorch" and "onnx" as format.')
collection = create_milvus(collection_name)
insert_count = insert(model_name, collection_name)
print('Total data inserted:', insert_count)
# collection = create_milvus(collection_name)
# insert_count = insert(model_name, collection_name)
# print('Total data inserted:', insert_count)
collection = Collection(collection_name)
benchmark = query(model_name, collection_name)

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