Add model conversion experiment code

experiments/model_conversion.py

+import models
+import torch
+import torch.optim as optim
+import argparse
+import data
+import models
+
+device = torch.device('cuda')
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--models', type=int, help='Number of models to use')
+parser.add_argument('--epochs', type=int, help='Number of epochs to train for')
+parser.add_argument('--dataset', choices=data.spatial_dataset_map.keys(), help='Dataset to use')
+parser.add_argument('--batch_size', type=int, help='Batch size')
+parser.add_argument('--data', help='Root folder for the dataset')
+args = parser.parse_args()
+
+spatial_accuracies = 0
+jpeg_accuracies = 0
+for m in range(args.models):
+    print('Train spatial model {}/{}'.format(m, args.models))
+    spatial_dataset = data.spatial_dataset_map[args.dataset](args.batch_size, args.data)
+    dataset_info = data.dataset_info[args.dataset]
+    spatial_model = models.SpatialResNet(dataset_info['channels'], dataset_info['classes']).to(device)
+    optimizer = optim.Adam(spatial_model.parameters())
+
+    for e in range(args.epochs):
+        models.train(spatial_model, device, spatial_dataset[0], optimizer, e)
+        models.test(spatial_model, device, spatial_dataset[1])
+
+    acc = models.test(spatial_model, device, spatial_dataset[1])
+    spatial_accuracies += acc
+
+    print('Convert model to JPEG')
+    jpeg_model = models.JpegResNetExact(spatial_model).to(device)
+
+    print('Test JPEG model')
+    jpeg_dataset = data.jpeg_dataset_map[args.dataset](args.batch_size, args.data)
+    acc = models.test(jpeg_model, device, jpeg_dataset[1])
+    jpeg_accuracies += acc
+
+spatial_accuracies /= args.models
+jpeg_accuracies /= args.models
+
+print('Report')
+print('======')
+print('Number of Models: {}'.format(args.n_models))
+print('Average Spatial Accuracy: {}'.format(spatial_accuracies))
+print('Average JPEG Accuracy: {}'.format(jpeg_accuracies))
+print('Deviation: {}'.format(spatial_accuracies - jpeg_accuracies))