Allow list as input

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

README.md

@@ -79,12 +79,12 @@ It uses the pre-trained model specified by model name to generate an image embed
 
 **Parameters:**
 
-***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)*
-
-The decoded image data in numpy.ndarray.
+***data:*** *Union[List[towhee._types.Image], towhee._types.Image]*
 
+The decoded image data in numpy.ndarray. It allows both single input and a list for batch input.
 
 
 **Returns:** *numpy.ndarray*
 
-The image embedding extracted by model.
+If only 1 image input, then output is an image embedding in shape of (feature_dim,).
+If a list of images as input, then output is a numpy.ndarray in shape of (batch_num, feature_dim).

timm_image.py

@@ -16,6 +16,7 @@ import logging
 import numpy
 import os
 from pathlib import Path
+from typing import List, Union
 
 import towhee
 from towhee.operator.base import NNOperator, OperatorFlag
@@ -62,20 +63,31 @@ class TimmImage(NNOperator):
         self.tfms = create_transform(**self.config)
         self.skip_tfms = skip_preprocess
 
-    @arg(1, to_image_color('RGB'))
-    def __call__(self, img: towhee._types.Image) -> numpy.ndarray:
-        img = PILImage.fromarray(img.astype('uint8'), 'RGB')
-        if not self.skip_tfms:
-            img = self.tfms(img).unsqueeze(0)
-        img = img.to(self.device)
-        features = self.model.forward_features(img)
+    def __call__(self, data: Union[List[towhee._types.Image], towhee._types.Image]):
+        if isinstance(data, list):
+            imgs = []
+            for img in data:
+                img = self.convert_img(img)
+                img = img if self.skip_tfms else self.tfms(img)
+                imgs.append(img)
+            inputs = torch.stack(imgs)
+        else:
+            img = self.convert_img(data)
+            img = img if self.skip_tfms else self.tfms(img)
+            inputs = img.unsqueeze(0)
+        inputs = inputs.to(self.device)
+        features = self.model.forward_features(inputs)
         if features.dim() == 4:
             global_pool = nn.AdaptiveAvgPool2d(1)
             features = global_pool(features)
 
-        features = features.to('cpu')
-        vec = features.flatten().detach().numpy()
-        return vec
+        vecs = features.to('cpu').flatten(1).squeeze(0).detach().numpy()
+        return vecs
+
+    @arg(1, to_image_color('RGB'))
+    def convert_img(self, img: towhee._types.Image):
+        img = PILImage.fromarray(img.astype('uint8'), 'RGB')
+        return img
 
     def save_model(self, format: str = 'pytorch', path: str = 'default'):
         if path == 'default':