update the ru-clip.

Signed-off-by: wxywb <xy.wang@zilliz.com>

README.md

@@ -1,2 +1,67 @@
-# ru-clip
+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+@register(output_schema=['vec'])
+class Clip(NNOperator): 
+    """
+    CLIP multi-modal embedding operator
+    """
+    def __init__(self, model_name: str, modality: str):
+        self.modality = modality
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = clip.create_model(model_name=model_name, pretrained=True, jit=True)
+        self.tokenize = clip.tokenize
+        self.tfms = transforms.Compose([
+            transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(
+               (0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+            ])
+
+    def inference_single_data(self, data):
+        if self.modality == 'image':
+            vec = self._inference_from_image(data)
+        elif self.modality == 'text': 
+            vec = self._inference_from_text(data)
+        else:
+            raise ValueError("modality[{}] not implemented.".format(self._modality))
+        return vec.detach().cpu().numpy().flatten()
+
+    def __call__(self, data):
+        if not isinstance(data, list):
+            data = [data]
+        else:
+            data = data
+        results = []
+        for single_data in data:
+            result = self.inference_single_data(single_data)
+            results.append(result)
+        if len(data) == 1:        
+            return results[0]
+        else:
+            return results
+
+    def _inference_from_text(self, text):
+        text = self.tokenize(text).to(self.device)
+        text_features = self.model.encode_text(text)
+        return text_features
+
+    @arg(1, to_image_color('RGB'))
+    def _inference_from_image(self, img):
+        img = to_pil(img)
+        image = self.tfms(img).unsqueeze(0).to(self.device)
+        image_features = self.model.encode_image(image)
+        return image_features
 

ru_clip.py

@@ -0,0 +1,77 @@
+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+from pathlib import Path
+import torch
+from torchvision import transforms
+
+from towhee.types.image_utils import to_pil
+from towhee.operator.base import NNOperator, OperatorFlag
+from towhee.types.arg import arg, to_image_color
+from towhee import register
+
+
+@register(output_schema=['vec'])
+class RuClip(NNOperator):
+    """
+    Russian CLIP multi-modal embedding operator
+    """
+    def __init__(self, model_name: str, modality: str):
+        self.modality = modality
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = clip.create_model(model_name=model_name, pretrained=True, jit=True)
+        self.tokenize = clip.tokenize
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        path = str(Path(__file__).parent)
+        sys.path.append(path)
+        import ruclip
+        sys.path.pop()
+        clip, processor = ruclip.load('ruclip-vit-base-patch32-384', device=self.device)
+        templates = ['{}', 'это {}', 'на картинке {}', 'это {}, домашнее животное']
+        self.predictor = ruclip.Predictor(clip, processor, device, bs=1, templates=templates)
+
+    def inference_single_data(self, data):
+        if self.modality == 'image':
+            vec = self._inference_from_image(data)
+        elif self.modality == 'text':
+            vec = self._inference_from_text(data)
+        else:
+            raise ValueError("modality[{}] not implemented.".format(self._modality))
+        return vec.detach().cpu().numpy().flatten()
+
+    def __call__(self, data):
+        if not isinstance(data, list):
+            data = [data]
+        else:
+            data = data
+        results = []
+        for single_data in data:
+            result = self.inference_single_data(single_data)
+            results.append(result)
+        if len(data) == 1:
+            return results[0]
+        else:
+            return results
+
+    def _inference_from_text(self, text):
+        text_features =  self.predictor.get_text_latents([text])
+        return text_features
+
+    @arg(1, to_image_color('RGB'))
+    def _inference_from_image(self, img):
+        img = to_pil(img)
+        image_features = self.predictor.get_image_latents([img])
+        return image_features