change the input format.

README.md

@@ -25,14 +25,14 @@ __init__(self, model_name: str = 'efficientnet-b7', framework: str = 'pytorch',
   - supported types: `str`, default is None, using pretrained weights
 
 ```python
-__call__(self, img_path: str)
+__call__(self,  image: 'towhee.types.Image')
 ```
 
 **Args:**
 
-- img_path:
-  - the input image path
-  - supported types: `str`
+- image:
+  - the input image
+  - supported types: `towhee.types.Image`
 
 **Returns:**
 

efficientnet_image_embedding.py

@@ -17,15 +17,15 @@ from PIL import Image
 import torch
 from torchvision import transforms
 import sys
+import towhee
 from pathlib import Path
 import numpy
 
 from towhee.operator import Operator
+from towhee.utils.pil_utils import to_pil
 from timm.data import resolve_data_config
 from timm.data.transforms_factory import create_transform
 import os
-import warnings
-warnings.filterwarnings("ignore")
 
 class EfficientnetImageEmbedding(Operator):
     """
@@ -51,8 +51,8 @@ class EfficientnetImageEmbedding(Operator):
         config = resolve_data_config({}, model=self.model._model)
         self.tfms = create_transform(**config)
 
-    def __call__(self, img_path: str) -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
+    def __call__(self, image: 'towhee.types.Image') -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
         Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
-        img = self.tfms(Image.open(img_path)).unsqueeze(0)
+        img = self.tfms(to_pil(image)).unsqueeze(0)
         features = self.model(img)
         return Outputs(features.flatten().detach().numpy())