Rajeev Rao
135 lines
5.2 KiB
Python
135 lines
5.2 KiB
Python
#
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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This file contains functions for training a PyTorch MNIST Model
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torchvision import datasets, transforms
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from torch.autograd import Variable
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import numpy as np
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import os
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from random import randint
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# Network
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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(1, 20, kernel_size=5)
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self.conv2 = nn.Conv2d(20, 50, kernel_size=5)
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self.fc1 = nn.Linear(800, 500)
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self.fc2 = nn.Linear(500, 10)
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def forward(self, x):
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x = F.max_pool2d(self.conv1(x), kernel_size=2, stride=2)
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x = F.max_pool2d(self.conv2(x), kernel_size=2, stride=2)
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x = x.view(-1, 800)
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return F.log_softmax(x, dim=1)
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class MnistModel(object):
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def __init__(self):
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self.batch_size = 64
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self.test_batch_size = 100
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self.learning_rate = 0.0025
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self.sgd_momentum = 0.9
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self.log_interval = 100
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# Fetch MNIST data set.
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self.train_loader = torch.utils.data.DataLoader(
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datasets.MNIST('/tmp/mnist/data', train=True, download=True, transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=self.batch_size,
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shuffle=True,
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num_workers=1,
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timeout=600)
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self.test_loader = torch.utils.data.DataLoader(
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datasets.MNIST('/tmp/mnist/data', train=False, transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=self.test_batch_size,
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shuffle=True,
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num_workers=1,
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timeout=600)
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self.network = Net()
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self.latest_test_accuracy = 0.0
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# Train the network for one or more epochs, validating after each epoch.
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def learn(self, num_epochs=2):
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# Train the network for a single epoch
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def train(epoch):
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self.network.train()
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optimizer = optim.SGD(self.network.parameters(), lr=self.learning_rate, momentum=self.sgd_momentum)
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for batch, (data, target) in enumerate(self.train_loader):
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data, target = Variable(data), Variable(target)
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optimizer.zero_grad()
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output = self.network(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch % self.log_interval == 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch * len(data), len(self.train_loader.dataset), 100. * batch / len(self.train_loader), loss.data.item()))
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# Test the network
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def test(epoch):
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self.network.eval()
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test_loss = 0
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correct = 0
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for data, target in self.test_loader:
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with torch.no_grad():
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data, target = Variable(data), Variable(target)
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output = self.network(data)
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test_loss += F.nll_loss(output, target).data.item()
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pred = output.data.max(1)[1]
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correct += pred.eq(target.data).cpu().sum()
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test_loss /= len(self.test_loader)
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self.latest_test_accuracy = float(correct) / len(self.test_loader.dataset)
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.1f}%)\n'.format(test_loss, correct, len(self.test_loader.dataset), 100. * self.latest_test_accuracy))
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for e in range(num_epochs):
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train(e + 1)
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test(e + 1)
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# @brief Get the latest accuracy on the test set
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# @pre self.learn.test (and thus self.learn()) need to be run
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def get_latest_test_set_accuracy(self):
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return self.latest_test_accuracy
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def get_weights(self):
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return self.network.state_dict()
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# Retrieve a single sample out of a batch and convert to flattened numpy array
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def convert_to_flattened_numpy_array(self, batch_data, batch_target, sample_idx):
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test_case = batch_data.numpy()[sample_idx].ravel().astype(np.float32)
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test_name = batch_target.numpy()[sample_idx]
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return test_case, test_name
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# Generator to loop over every sample in the test set, sample by sample
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def get_all_test_samples(self):
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for data, target in self.test_loader:
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for case_num in range(len(data)):
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yield self.convert_to_flattened_numpy_array(data, target, case_num)
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