add mnist_rnn to test script for CI

mnist_rnn/main.py

@@ -1,11 +1,13 @@
 from __future__ import print_function
+
 import argparse
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import StepLR
+from torchvision import datasets, transforms
 
 
 class Net(nn.Module):
@@ -51,9 +53,11 @@ def train(args, model, device, train_loader, optimizer, epoch):
             print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                 epoch, batch_idx * len(data), len(train_loader.dataset),
                        100. * batch_idx / len(train_loader), loss.item()))
+            if args.dry_run:
+                break
 
 
-def test(model, device, test_loader):
+def test(args, model, device, test_loader):
     model.eval()
     test_loss = 0
     correct = 0
@@ -64,6 +68,8 @@ def test(model, device, test_loader):
             test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
             pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
             correct += pred.eq(target.view_as(pred)).sum().item()
+            if args.dry_run:
+                break
 
     test_loss /= len(test_loader.dataset)
 
@@ -87,6 +93,8 @@ def main():
                         help='learning rate step gamma (default: 0.7)')
     parser.add_argument('--no-cuda', action='store_true', default=False,
                         help='disables CUDA training')
+    parser.add_argument('--dry-run', action='store_true', default=False,
+                        help='quickly check a single pass')
     parser.add_argument('--seed', type=int, default=1, metavar='S',
                         help='random seed (default: 1)')
     parser.add_argument('--log-interval', type=int, default=10, metavar='N',
@@ -121,7 +129,7 @@ def main():
     scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
     for epoch in range(1, args.epochs + 1):
         train(args, model, device, train_loader, optimizer, epoch)
-        test(model, device, test_loader)
+        test(args, model, device, test_loader)
         scheduler.step()
 
     if args.save_model:

run_python_examples.sh

@@ -99,6 +99,11 @@ function mnist_hogwild() {
   python main.py --epochs 1 --dry-run $CUDA_FLAG || error "mnist hogwild failed"
 }
 
+function mnist_rnn() {
+  start
+  python main.py --epochs 1 --dry-run || error "mnist rnn example failed"
+}
+
 function regression() {
   start
   python main.py --epochs 1 $CUDA_FLAG || error "regression failed"
@@ -190,6 +195,7 @@ function run_all() {
   imagenet
   mnist
   mnist_hogwild
+  mnist_rnn
   regression
   reinforcement_learning
   siamese_network