[examples] copy resnet example to image (#4090)

* copy resnet example

* add pytest package

* skip test_ci

* skip test_ci

* skip test_ci

examples/images/resnet/eval.py

@@ -0,0 +1,48 @@
+import argparse
+
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+# ==============================
+# Parse Arguments
+# ==============================
+parser = argparse.ArgumentParser()
+parser.add_argument('-e', '--epoch', type=int, default=80, help="resume from the epoch's checkpoint")
+parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory")
+args = parser.parse_args()
+
+# ==============================
+# Prepare Test Dataset
+# ==============================
+# CIFAR-10 dataset
+test_dataset = torchvision.datasets.CIFAR10(root='./data/', train=False, transform=transforms.ToTensor())
+
+# Data loader
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=128, shuffle=False)
+
+# ==============================
+# Load Model
+# ==============================
+model = torchvision.models.resnet18(num_classes=10).cuda()
+state_dict = torch.load(f'{args.checkpoint}/model_{args.epoch}.pth')
+model.load_state_dict(state_dict)
+
+# ==============================
+# Run Evaluation
+# ==============================
+model.eval()
+
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.cuda()
+        labels = labels.cuda()
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))