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Making CUDA optional for MNIST example #3

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merged 1 commit into from
Sep 25, 2016
Merged

Making CUDA optional for MNIST example #3

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1 change: 1 addition & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
@@ -1 +1,2 @@
mnist/data
*.pyc
34 changes: 26 additions & 8 deletions mnist/main.py
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Expand Up @@ -2,7 +2,14 @@
import os
import torch
import torch.nn as nn
import torch.nn.cuda

try:
import torch.nn.cuda
cuda = True
except ImportError:
print('Could not import CUDA, skipping')
cuda = False

import torch.optim as optim
from torch.autograd import Variable
from tqdm import tqdm
Expand Down Expand Up @@ -51,9 +58,11 @@ def __call__(self, x):
x = self.relu(self.fc2(x))
return self.softmax(x)

model = Net().cuda()
model = Net()
if cuda == True:
model.cuda()

criterion = nn.ClassNLLCriterion()
criterion = nn.NLLLoss()

# Training settings
BATCH_SIZE = 150
Expand All @@ -63,8 +72,13 @@ def __call__(self, x):
optimizer = optim.SGD(model, lr=1e-2, momentum=0.9)

def train(epoch):
batch_data = Variable(torch.cuda.FloatTensor(BATCH_SIZE, 1, 28, 28), requires_grad=False)
batch_targets = Variable(torch.cuda.FloatTensor(BATCH_SIZE), requires_grad=False)
batch_data_t = torch.FloatTensor(BATCH_SIZE, 1, 28, 28)
batch_targets_t = torch.LongTensor(BATCH_SIZE)
if cuda:
batch_data_t = batch_data_t.cuda()
batch_targets_t = batch_targets_t.cuda()
batch_data = Variable(batch_data_t, requires_grad=False)
batch_targets = Variable(batch_targets_t, requires_grad=False)
for i in range(0, training_data.size(0), BATCH_SIZE):
batch_data.data[:] = training_data[i:i+BATCH_SIZE]
batch_targets.data[:] = training_labels[i:i+BATCH_SIZE]
Expand All @@ -76,8 +90,13 @@ def train(epoch):

def test(epoch):
test_loss = 0
batch_data = Variable(torch.cuda.FloatTensor(TEST_BATCH_SIZE, 1, 28, 28), volatile=True)
batch_targets = Variable(torch.cuda.FloatTensor(TEST_BATCH_SIZE), volatile=True)
batch_data_t = torch.FloatTensor(TEST_BATCH_SIZE, 1, 28, 28)
batch_targets_t = torch.LongTensor(TEST_BATCH_SIZE)
if cuda:
batch_data_t = batch_data_t.cuda()
batch_targets_t = batch_targets_t.cuda()
batch_data = Variable(batch_data_t, volatile=True)
batch_targets = Variable(batch_targets_t, volatile=True)
correct = 0
for i in range(0, test_data.size(0), TEST_BATCH_SIZE):
print('Testing model: {}/{}'.format(i, test_data.size(0)), end='\r')
Expand All @@ -98,4 +117,3 @@ def test(epoch):
for epoch in range(1, NUM_EPOCHS+1):
train(epoch)
test(epoch)