update samples from Release-94 as a part of SDK release (#1418)

Co-authored-by: amlrelsa-ms <amlrelsa@microsoft.com>

tutorials/quickstart/score.py

@@ -0,0 +1,51 @@
+import os
+import torch
+import json
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+def init():
+    global net
+    global classes
+
+    model_filename = 'cifar_net.pth'
+    model_path = os.path.join(os.environ['AZUREML_MODEL_DIR'], model_filename)
+    net = Net()
+    net.load_state_dict(torch.load(model_path))
+    classes = ('plane', 'car', 'bird', 'cat',
+               'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+
+def run(data):
+    data = json.loads(data)
+    images = torch.FloatTensor(data['data'])
+    outputs = net(images)
+
+    _, predicted = torch.max(outputs, 1)
+
+    result = [classes[predicted[j]] for j in range(4)]
+    result_json = json.dumps({"predictions": result})
+
+    # You can return any JSON-serializable object.
+    return result_json