condition and supervise

Author: lucidtronix

ml4h/models/train.py

@@ -178,14 +178,17 @@ def train_diffusion_model(args):
         metrics = model.evaluate(generate_test, batch_size=args.batch_size, steps=args.test_steps, return_dict=True)
         logging.info(f'Test metrics: {metrics}')
 
-        data, labels, paths = big_batch_from_minibatch_generator(generate_test, args.test_steps)
-        preds = model.plot_reconstructions((data, labels), prefix=f'{args.output_folder}/{args.id}/reconstructions/')
+        steps = 1 if args.batch_size > 3 else args.test_steps
+        data, labels, paths = big_batch_from_minibatch_generator(generate_test, steps)
+        sides = int(args.batch_size)
+        preds = model.plot_reconstructions((data, labels), num_rows=sides, num_cols=sides,
+                                           prefix=f'{args.output_folder}/{args.id}/reconstructions/')
         image_out = {args.tensor_maps_in[0].output_name(): data[args.tensor_maps_in[0].input_name()]}
         predictions_to_pngs(preds, args.tensor_maps_in, args.tensor_maps_in, data, image_out, paths, f'{args.output_folder}/{args.id}/reconstructions/')
         if model.tensor_map.axes() == 2:
             model.plot_ecgs(num_rows=4, prefix=os.path.dirname(checkpoint_path))
         else:
-            model.plot_images(num_cols=min(4,args.batch_size), num_rows=min(4,args.test_steps), prefix=os.path.dirname(checkpoint_path))
+            model.plot_images(num_cols=sides, num_rows=sides, prefix=os.path.dirname(checkpoint_path))
     return model