implementations

README.md

@@ -1,3 +1,59 @@
-# efficientnet-embedding
+# Efficientnet Embedding Operator
+
+Authors: kyle he
+
+## Overview
+
+EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models[1], which is trained on [imagenet dataset](https://image-net.org/download.php).
+
+
+## Interface
+
+```python
+__init__(self, model_name: str = 'efficientnet-b7', framework: str = 'pytorch', weights_path: str = None)
+```
+
+**Args:**
+
+- model_name:
+  - the model name for embedding
+  - supported types: `str`, for example 'efficientnet-b7'
+- framework:
+  - the framework of the model
+  - supported types: `str`, default is 'pytorch'
+- weights_path:
+  - the weights path
+  - supported types: `str`, default is None, using pretrained weights
+
+```python
+__call__(self, img_path: str)
+```
+
+**Args:**
+
+- img_path:
+  - the input image path
+  - supported types: `str`
+
+**Returns:**
+
+The Operator returns a tuple `Tuple[('feature_vector', numpy.ndarray)]` containing following fields:
+
+- feature_vector:
+  - the embedding of the image
+  - data type: `numpy.ndarray`
+
+## Requirements
+
+You can get the required python package by [requirements.txt](./requirements.txt).
+
+## How it works
+
+The `towhee/efficientnet-embedding` Operator implements the function of image embedding, which can add to the pipeline. For example, it's the key Operator named embedding_model within [efficientnet-embedding](https://hub.towhee.io/towhee/efficientnet-embedding) pipeline.
+
+
+
+## Reference
+
+[1].https://github.com/lukemelas/EfficientNet-PyTorch#example-feature-extraction
 
-This is another test repo

__init__.py

@@ -0,0 +1,21 @@
+# 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 efficientnet_pytorch
+except ModuleNotFoundError:
+    os.system('pip install efficientnet_pytorch')

efficientnet_embedding.py

@@ -0,0 +1,48 @@
+# 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.
+
+from typing import NamedTuple
+from PIL import Image
+import torch
+from torchvision import transforms
+import sys
+from pathlib import Path
+
+from towhee.operator import Operator
+
+
+class EfficientnetEmbeddingOperator(Operator):
+    """
+    Embedding extractor using efficientnet.
+    Args:
+        model_name (`string`):
+            Model name.
+        weights_path (`string`):
+            Path to local weights.
+    """
+
+    def __init__(self, model_name: str = 'efficientnet-b7', framework: str = 'pytorch', weights_path: str = None) -> None:
+        super().__init__()
+        sys.path.append(str(Path(__file__).parent))
+        if framework == 'pytorch':
+            from pytorch.model import Model
+        self.model = Model(model_name, weights_path)
+        self.tfms = transforms.Compose([transforms.Resize(224), transforms.ToTensor(),
+                                        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ])
+
+    def __call__(self, img_path: str) -> NamedTuple('Outputs', [('embedding', torch.Tensor)]):
+        Outputs = NamedTuple('Outputs', [('embedding', torch.Tensor)])
+        img = self.tfms(Image.open(img_path)).unsqueeze(0)
+        features = self.model._model.extract_features(img)
+        return Outputs(features.flatten().detach().numpy())

efficientnet_embedding.yaml

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+name: 'efficientnet-embedding'
+labels: 
+  recommended_framework: pytorch1.2.0
+  class: image-embedding
+  others: efficientnet
+operator: 'towhee/efficientnet-embedding'
+init:
+  model_name: str
+call:
+  input:
+    img_path: str
+  output:
+    feature_vector: numpy.ndarray

pytorch/__init__.py

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+# 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.

pytorch/model.py

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+# 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.
+
+
+from typing import NamedTuple
+
+import numpy
+import torch
+from efficientnet_pytorch import EfficientNet
+
+
+class Model():
+    """
+    PyTorch model class
+    """
+    def __init__(self, model_name: str, weights_path: str):
+        super().__init__()
+        self._model = EfficientNet.from_pretrained(model_name=model_name, weights_path=weights_path)
+        self._model.eval()
+
+    def __call__(self, img_tensor: torch.Tensor):
+        return self._model(img_tensor).detach().numpy()
+
+    def train(self):
+        """
+        For training model
+        """
+        pass