inference.py

from __future__ import print_function

import os
import torch
import torch.nn as nn 
import torch.nn.functional as F

## This is the entry point file - it defines the model definition.

## This is the architecture, and then we load this dictionary.


# Network definition
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 model_fn(model_dir):
    device = "cpu"
    model = Net()
    
    with open(os.path.join(model_dir, "model.pth"), "rb") as f:
        model.load_state_dict(torch.load(f, map_location=device))
    return model.to(device)