Add files

LICENSE

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+MIT License
+
+Copyright (c) 2022 nbarlowATI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

catdog_classification_model/__init__.py

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+# from .model import

catdog_classification_model/model.py

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+import os
+import requests 
+import torch
+from torchvision import transforms
+import numpy as np
+
+CLASS_LABELS = ["cat", "dog"]
+
+class CatDogClassifier:
+     def __init__(self):
+        filename = "pet_conv_net_model.pth"
+        if not os.path.exists(filename):
+            model_path = os.path.join("https://connectionsworkshop.blob.core.windows.net/pets", filename)
+            r = requests.get(model_path)
+            with open(filename, "wb") as outfile:
+                outfile.write(r.content)
+        # self.model = tf.keras.models.load_model(filename)
+        self.model = torch.load(filename)
+
+
+     def predict(self, input):
+        # transform input image (as required by model)
+        transform_input = transforms.Compose([ transforms.Resize((256,256)), transforms.Normalize(mean=[ 82.18, 139.30, 140.27],std=[49.40, 35.24, 37.86],) ]) 
+        input = input[:,:,0:3] # make sure we have only 3 channels
+        input = np.transpose(input, (2, 0, 1))
+        input = torch.from_numpy(input).type(torch.float32),
+        input = transform_input(input)
+
+        # make prediction
+        prediction = self.model(input) # TODO: is it model.predict() for tf?
+        prediction = prediction.detach().numpy()
+        max_val = np.max(prediction)
+        max_ind = np.argmax(prediction)
+        return f"{CLASS_LABELS[max_ind]}"
+

requirements.txt

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+torch
+torchvision
+numpy

setup.py

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+#!/usr/bin/env python
+from setuptools import find_packages, setup
+
+requirements = []
+with open("requirements.txt") as f:
+    for line in f:
+        stripped = line.split("#")[0].strip()
+        if len(stripped) > 0:
+            requirements.append(stripped)
+
+setup(
+    name="catdog_classification_model",
+    version="0.0.1",
+    description="scivision plugin, using a cat/dog classifier CNN model",
+    url="https://github.com/pwochner/catdog_classification_model",
+    packages=find_packages(),
+    install_requires=requirements,
+    python_requires=">=3.7",
+)