Add transform-image op

.gitattributes

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+# .gitattributes
+
+# Source files
+# ============
+*.pxd    text diff=python
+*.py     text diff=python
+*.py3    text diff=python
+*.pyw    text diff=python
+*.pyx    text diff=python
+*.pyz    text diff=python
+*.pyi    text diff=python
+
+# Binary files
+# ============
+*.db     binary
+*.p      binary
+*.pkl    binary
+*.pickle binary
+*.pyc    binary export-ignore
+*.pyo    binary export-ignore
+*.pyd    binary
+
+# Jupyter notebook
+*.ipynb  text
+
+# Model files
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

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-# transform-image
+# Transform Image Operator
 
-This is another test repo
+Authors: name or github-name(email)
+
+## Overview
+
+Introduce the functions of op and the model used.
+
+## Interface
+
+The interface of all the functions in op. (input & output)
+
+## How to use
+
+- Requirements from requirements.txt
+- How it works in some typical pipelines and the yaml example.
+
+## Reference
+
+Model link.

__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.

requirements.txt

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+torch>=1.2.0
+torchvision>=0.4.0
+numpy>=1.19.5
+pillow>=8.3.1

test_transform_image.py

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+import unittest
+from PIL import Image
+from torchvision import transforms
+from transform_image import TransformImage
+
+
+class TestTransformImage(unittest.TestCase):
+    test_img = './test_data/test.jpg'
+    test_img = Image.open(test_img)
+    tfms = transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    img1 = tfms(test_img).unsqueeze(0)
+
+    def test_transform_image(self):
+        op = TransformImage(256)
+        outputs = op(self.test_img)
+        print("The output tyep of operator:", type(outputs.img_transformed))
+        c = (self.img1.numpy() == outputs.img_transformed.numpy())
+        self.assertEqual(c.all(), True)
+
+
+if __name__ == '__main__':
+    unittest.main()

transform_image.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, Union
+
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+
+from towhee.operator import Operator
+
+
+class TransformImage(Operator):
+    """
+    Use PyTorch to transform an image (resize, crop, normalize, etc...)
+
+    Args:
+        size (`int`):
+            Image size to use. A resize to `size x size` followed by center crop and
+            image normalization will be done.
+    """
+    def __init__(self, size: int) -> None:
+        super().__init__()
+        # user defined transform
+        self.tfms = transforms.Compose(
+            [
+                transforms.Resize(size),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+            ]
+        )
+
+    def __call__(self, img_tensor: Union[np.ndarray, Image.Image, torch.Tensor, str]) -> NamedTuple('Outputs', [('img_transformed', torch.Tensor)]):
+        """
+        Call it when use this class.
+        
+        Args:
+            img_tensor (`Union[np.ndarray, Image.Image, torch.Tensor, str]`):
+                The image data to be normalized, you can try one of the 
+                four formats: np.ndarray, Image.Image, torch.Tensor and str.
+        Returns:
+            (`torch.Tensor`)
+                The normalized image tensor.
+        """
+        if isinstance(img_tensor, str):
+            img_tensor = Image.open(img_tensor)
+        if isinstance(img_tensor, Image.Image):
+            img_tensor = img_tensor.convert('RGB')
+        Outputs = NamedTuple('Outputs', [('img_transformed', torch.Tensor)])
+        return Outputs(self.tfms(img_tensor).unsqueeze(0))

transform_image.yaml

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+name: 'transform-image'
+labels: 
+  recommended_framework: pytorch1.2.0
+  class: image-transform
+  others:
+operator: 'towhee/transform-image'
+init:
+  size: int
+call:
+  input:
+    img_tensor: Union[np.ndarray, Image.Image, torch.Tensor, str]
+  output:
+    img_transformed: torch.Tensor