Add benchmark script

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>

benchmark/run.py

@@ -0,0 +1,172 @@
+import os
+import torch
+import onnxruntime
+
+import towhee
+from towhee import ops
+from pymilvus import connections, DataType, FieldSchema, Collection, CollectionSchema, utility
+from datasets import load_dataset
+
+from statistics import mode
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', required=True, type=str)
+parser.add_argument('--dataset', type=str, default='mnist')
+parser.add_argument('--insert_size', type=int, default=1000)
+parser.add_argument('--query_size', type=int, default=100)
+parser.add_argument('--topk', type=int, default=10)
+parser.add_argument('--collection_name', type=str, default=None)
+parser.add_argument('--format', type=str, required=True)
+parser.add_argument('--onnx_dir', type=str, default='./saved/onnx')
+
+args = parser.parse_args()
+model_name = args.model
+onnx_path = os.path.join(args.onnx_dir, model_name.replace('/', '-') + '.onnx')
+dataset_name = args.dataset
+insert_size = args.insert_size
+query_size = args.query_size
+topk = args.topk
+collection_name = args.collection_name if args.collection_name else model_name.replace('-', '_').replace('/', '_')
+onnx_path = os.path.join(args.onnx_dir, model_name.replace('/', '_') + '.onnx')
+
+device = 'cpu'
+host = 'localhost'
+port = '19530'
+index_type = 'FLAT'
+metric_type = 'L2'
+
+data = load_dataset(dataset_name).shuffle(seed=32)
+assert insert_size <= len(data['train']), 'There is no enough data. Please decrease insert size.'
+assert insert_size <= len(data['test']), 'There is no enough data. Please decrease query size.'
+
+insert_data = data['train']
+insert_data = insert_data[:insert_size] if insert_size else insert_data
+query_data = data['test']
+query_data = query_data[:query_size] if query_size else query_data
+
+# Warm up
+print('Warming up...')
+op = ops.image_embedding.timm(model_name=model_name, device=device).get_op()
+dummy_input = torch.rand((1,) + op.config['input_size'])
+dim = op(dummy_input).shape[0]
+print(f'output dim: {dim}')
+
+# Prepare Milvus
+print('Connecting milvus ...')
+connections.connect(host=host, port=port)
+
+
+def create_milvus(collection_name):
+    print('Creating collection ...')
+    fields = [
+        FieldSchema(name='id', dtype=DataType.INT64, description='embedding id', is_primary=True, auto_id=True),
+        FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, description='image embedding', dim=dim),
+        FieldSchema(name='label', dtype=DataType.VARCHAR, description='label', max_length=500)
+    ]
+    schema = CollectionSchema(fields=fields, description=f'image embeddings for {model_name} on {dataset_name}')
+    if utility.has_collection(collection_name):
+        print(f'drop old collection: {collection_name}')
+        collection = Collection(collection_name)
+        collection.drop()
+    collection = Collection(name=collection_name, schema=schema)
+    print(f'A new collection is created: {collection_name}.')
+    return collection
+
+
+if args.format == 'pytorch':
+    collection_name = collection_name + '_pytorch'
+
+    def insert(model_name, collection_name):
+        (
+            towhee.dc['image', 'label'](zip(insert_data['image'], insert_data['label'])).stream()
+                  .image_embedding.timm['image', 'emb'](model_name=model_name, device=device)
+                  .runas_op['label', 'label'](lambda y: str(y))
+                  .ann_insert.milvus[('emb', 'label'), 'miluvs_insert'](
+                        uri=f'tcp://{host}:{port}/{collection_name}'
+                        )
+                  .show(3)
+        )
+        collection = Collection(collection_name)
+        return collection.num_entities
+
+    def query(model_name, collection_name):
+        benchmark = (
+            towhee.dc['image', 'gt'](zip(query_data['image'], query_data['label'])).stream()
+                  .image_embedding.timm['image', 'emb'](model_name=model_name, device=device)
+                  .runas_op['gt', 'gt'](lambda y: str(y))
+                  .ann_search.milvus['emb', 'milvus_res'](
+                        uri=f'tcp://{host}:{port}/{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['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]))
+                  .with_metrics(['accuracy'])
+                  .evaluate['gt', 'pred1']('pred1')
+                  .evaluate['gt', 'pred5']('pred5')
+                  .evaluate['gt', 'pred10']('pred10')
+                  .report()
+        )
+        return benchmark
+elif args.format == 'onnx':
+    collection_name = collection_name + '_onnx'
+    if not os.path.exists(onnx_path):
+        onnx_path = op.save_model(format='onnx')
+
+    @towhee.register
+    def run_onnx(img):
+        img = op.tfms(img).cpu().detach().numpy()
+        features = sess.run(output_names=['output_0'], input_feed={'input_0': img})[0]
+        if len(features.shape) == 4:
+            global_pool = nn.AdaptiveAvgPool2d(1)
+            features = global_pool(features)
+        outs = features.squeeze(0)
+        assert outs.shape[0] == dim, 'Output dimensions are not consistent.'
+        return outs.cpu().detach().numpy()
+
+    def insert(model_name, collection_name):
+        (
+            towhee.dc['image', 'label'](zip(insert_data['image'], insert_data['label'])).stream()
+                  .run_onnx['image', 'emb']()
+                  .runas_op['label', 'label'](lambda y: str(y))
+                  .ann_insert.milvus[('emb', 'label'), 'miluvs_insert'](
+                        uri=f'tcp://{host}:{port}/{collection_name}'
+                        )
+                  .show(3)
+        )
+        collection = Collection(collection_name)
+        return collection.num_entities
+
+    def query(model_name, collection_name):
+        benchmark = (
+            towhee.dc['image', 'gt'](zip(query_data['image'], query_data['label'])).stream()
+                  .run_onnx['image', 'emb']()
+                  .runas_op['gt', 'gt'](lambda y: str(y))
+                  .ann_search.milvus['emb', 'milvus_res'](
+                           uri=f'tcp://{host}:{port}/{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['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]))
+                  .with_metrics(['accuracy'])
+                  .evaluate['gt', 'pred1']('pred1')
+                  .evaluate['gt', 'pred5']('pred5')
+                  .evaluate['gt', 'pred10']('pred10')
+                  .report()
+        )
+        return benchmark
+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)
+benchmark = query(model_name, collection_name)

timm_image.py

@@ -83,14 +83,14 @@ class TimmImage(NNOperator):
             imgs = data
         img_list = []
         for img in imgs:
-            img = self.convert_img(img)
+            img = self.convert_img(img) if isinstance(img, numpy.ndarray) else img
             img = img if self.skip_tfms else self.tfms(img)
             img_list.append(img)
         inputs = torch.stack(img_list)
         inputs = inputs.to(self.device)
         features = self.model.forward_features(inputs)
         if features.dim() == 4:
-            global_pool = nn.AdaptiveAvgPool2d(1)
+            global_pool = nn.AdaptiveAvgPool2d(1).to(self.device)
             features = global_pool(features)
         features = features.to('cpu').flatten(1)
         if isinstance(data, list):
@@ -135,7 +135,7 @@ class TimmImage(NNOperator):
                                   path,
                                   input_names=['input_0'],
                                   output_names=['output_0'],
-                                  opset_version=14,
+                                  opset_version=12,
                                   dynamic_axes={
                                       'input_0': {0: 'batch_size'},
                                       'output_0': {0: 'batch_size'}
@@ -148,6 +148,7 @@ class TimmImage(NNOperator):
         # todo: elif format == 'tensorrt':
         else:
             log.error(f'Unsupported format "{format}".')
+        return Path(path).resolve()
 
     @staticmethod
     def supported_model_names(format: str = None):