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