Fix throughput experiments

experiments/jpeg_throughput.py

@@ -20,19 +20,21 @@ jpeg_model = models.JpegResNet(spatial_model, n_freqs=6).to(device)
 optimizer = optim.Adam(jpeg_model.parameters())
 
 
-t0 = time.perf_counter()
+t0 = time.time()
 models.train(jpeg_model, device, dataset[0], optimizer, 0)
 torch.cuda.synchronize()
-t1 = time.perf_counter()
+t1 = time.time()
 
 training_time = t1 - t0
 
-t0 = time.perf_counter()
+jpeg_model.explode_all()
+
+t0 = time.time()
 models.test(jpeg_model, device, dataset[1])
 torch.cuda.synchronize()
-t1 = time.perf_counter()
+t1 = time.time()
 testing_time = t1 - t0
 
 with open('{}_jpeg_throughput.csv'.format(args.dataset), 'w') as f:
     f.write('Training, Testing\n')
-    f.write('{}, {}\n'.format(training_time / len(dataset[0]), testing_time / len(dataset[1])))
+    f.write('{}, {}\n'.format(len(dataset[0]) / training_time, len(dataset[1]) / testing_time))

experiments/spatial_throughput.py

@@ -20,19 +20,19 @@ spatial_model = models.SpatialResNet(dataset_info['channels'], dataset_info['cla
 optimizer = optim.Adam(spatial_model.parameters())
 
 
-t0 = time.perf_counter()
+t0 = time.time()
 models.train(spatial_model, device, dataset[0], optimizer, 0, do_decode=True)
 torch.cuda.synchronize()
-t1 = time.perf_counter()
+t1 = time.time()
 
 training_time = t1 - t0
 
-t0 = time.perf_counter()
+t0 = time.time()
 models.test(spatial_model, device, dataset[1], do_decode=True)
 torch.cuda.synchronize()
-t1 = time.perf_counter()
+t1 = time.time()
 testing_time = t1 - t0
 
 with open('{}_spatial_throughput.csv'.format(args.dataset), 'w') as f:
     f.write('Training, Testing\n')
-    f.write('{}, {}\n'.format(training_time / len(dataset[0]), testing_time / len(dataset[1])))
+    f.write('{}, {}\n'.format(len(dataset[0]) / training_time, len(dataset[1]) / testing_time ))