Updated notebook folders

training/01.train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py

@@ -1,57 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-import torch
-import torch.nn as nn
-from torchvision import transforms
-import json
-import base64
-from io import BytesIO
-from PIL import Image
-
-from azureml.core.model import Model
-
-
-def preprocess_image(image_file):
-    """Preprocess the input image."""
-    data_transforms = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ])
-
-    image = Image.open(image_file)
-    image = data_transforms(image).float()
-    image = torch.tensor(image)
-    image = image.unsqueeze(0)
-    return image
-
-
-def base64ToImg(base64ImgString):
-    base64Img = base64ImgString.encode('utf-8')
-    decoded_img = base64.b64decode(base64Img)
-    return BytesIO(decoded_img)
-
-
-def init():
-    global model
-    model_path = Model.get_model_path('pytorch-hymenoptera')
-    model = torch.load(model_path, map_location=lambda storage, loc: storage)
-    model.eval()
-
-
-def run(input_data):
-    img = base64ToImg(json.loads(input_data)['data'])
-    img = preprocess_image(img)
-
-    # get prediction
-    output = model(img)
-
-    classes = ['ants', 'bees']
-    softmax = nn.Softmax(dim=1)
-    pred_probs = softmax(model(img)).detach().numpy()[0]
-    index = torch.argmax(output, 1)
-
-    result = {"label": classes[index], "probability": str(pred_probs[index])}
-    return result