remove get_model and pytorch folder.

pytorch/__init__.py

@@ -1,24 +0,0 @@
-# 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 os
-
-# For requirements.
-try:
-    import timm
-except ModuleNotFoundError:
-    os.system('pip install timm')
-
-from timm.data import resolve_data_config
-from timm.data.transforms_factory import create_transform

pytorch/model.py

@@ -1,42 +0,0 @@
-# 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 torch
-from torch.nn import Linear
-from torch import nn
-import timm
-
-
-class Model():
-    """
-    PyTorch model class
-    """
-    def __init__(self, model_name: str, weights_path: str, num_classes=1000):
-        super().__init__()
-        if weights_path:
-            self._model = timm.create_model(model_name, checkpoint_path=weights_path, num_classes=num_classes)
-        else:
-            self._model = timm.create_model(model_name, pretrained=True, num_classes=num_classes)
-        self._model.eval()
-
-    def __call__(self, img_tensor: torch.Tensor):
-        self._model.eval()
-        features = self._model.forward_features(img_tensor)
-        if features.dim() == 4:  # if the shape of feature map is [N, C, H, W], where H > 1 and W > 1
-            global_pool = nn.AdaptiveAvgPool2d(1)
-            features = global_pool(features)
-        return features.flatten().detach().numpy()
-
-

vit_image_embedding.py

@@ -13,23 +13,21 @@
 # limitations under the License.
 
 
-import sys
-from typing import NamedTuple
-from pathlib import Path
-from PIL import Image
+import timm
 import torch
-from torch import nn as nn
 import numpy
-import os
-
-from towhee.operator import Operator, NNOperator
+from torch import nn as nn
+from typing import NamedTuple
+from towhee.operator import NNOperator
 from timm.data import resolve_data_config
 from timm.data.transforms_factory import create_transform
 from towhee.utils.pil_utils import to_pil
 
 import warnings
+
 warnings.filterwarnings("ignore")
 
+
 class VitImageEmbedding(NNOperator):
     """
     Embedding extractor using ViT.
@@ -42,23 +40,25 @@ class VitImageEmbedding(NNOperator):
 
     def __init__(self, model_name: str = 'vit_large_patch16_224', num_classes: int = 1000,
                  framework: str = 'pytorch', weights_path: str = None) -> None:
-        super().__init__()
-        if framework == 'pytorch':
-            import importlib.util
-            path = os.path.join(str(Path(__file__).parent), 'pytorch', 'model.py')
-            opname = os.path.basename(str(Path(__file__))).split('.')[0]
-            spec = importlib.util.spec_from_file_location(opname, path)
-            module = importlib.util.module_from_spec(spec)
-            spec.loader.exec_module(module)
-        self.model = module.Model(model_name, weights_path, num_classes=num_classes)
-        config = resolve_data_config({}, model=self.model._model)
+        super().__init__(framework=framework)
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        if weights_path:
+            self.model = timm.create_model(model_name, checkpoint_path=weights_path, num_classes=num_classes)
+        else:
+            self.model = timm.create_model(model_name, pretrained=True, num_classes=num_classes)
+        self.model.eval()
+        config = resolve_data_config({}, model=self.model)
         self.tfms = create_transform(**config)
 
     def __call__(self, image: 'towhee.types.Image') -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
-        img = self.tfms(to_pil(image)).unsqueeze(0)
+        img_tensor = self.tfms(to_pil(image)).unsqueeze(0)
         Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
-        features = self.model(img)
+        self.model.to(self.device)
+        self.model.eval()
+        features = self.model.forward_features(img_tensor)
+        if features.dim() == 4:  # if the shape of feature map is [N, C, H, W], where H > 1 and W > 1
+            global_pool = nn.AdaptiveAvgPool2d(1)
+            features = global_pool(features)
+        features = features.to('cpu')
+        features = features.flatten().detach().numpy()
         return Outputs(features)
-
-    def get_model(self) -> nn.Module:
-        return self.model._model