Add relu training experiment

experiments/relu_training.py

+import models
+import torch
+import torch.optim as optim
+import argparse
+import data
+
+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
+asm_accuracies = torch.zeros(15)
+apx_accuracies = torch.zeros(15)
+for m in range(args.models):
+    print('Train spatial model {}/{}'.format(m+1, 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)
+
+    acc = models.test(spatial_model, device, spatial_dataset[1])
+    spatial_accuracies += acc
+
+    jpeg_dataset = data.jpeg_dataset_map[args.dataset](args.batch_size, args.data)
+    for f in range(15):
+        print('Train ASM JPEG with {} spatial frequencies'.format(f))
+        jpeg_model = models.JpegResNet(models.SpatialResNet(dataset_info['channels'], dataset_info['classes']), n_freqs=f).to(device)
+        optimizer = optim.Adam(jpeg_model.parameters())
+
+        for e in range(args.epochs):
+            models.train(jpeg_model, device, jpeg_dataset[0], optimizer, e)
+
+        acc = models.test(jpeg_model, device, jpeg_dataset[1])
+        asm_accuracies[f] += acc
+
+        print('Train APX JPEG with {} spatial frequencies'.format(f))
+        jpeg_model = models.JpegResNetApx(models.SpatialResNet(dataset_info['channels'], dataset_info['classes']), n_freqs=f).to(device)
+        optimizer = optim.Adam(jpeg_model.parameters())
+        for e in range(args.epochs):
+            models.train(jpeg_model, device, jpeg_dataset[0], optimizer, e)
+
+        acc = models.test(jpeg_model, device, jpeg_dataset[1])
+        apx_accuracies[f] += acc
+
+spatial_accuracies /= args.models
+asm_accuracies /= args.models
+apx_accuracies /= args.models
+
+with open('{}_relu_training.csv'.format(args.dataset), 'w') as f:
+    f.write('Spatial, ASM, APX\n')
+    for i in range(15):
+        f.write('{}, {}, {}\n'.format(spatial_accuracies, asm_accuracies[i], apx_accuracies[i]))