condition and supervise

ml4h/models/diffusion_blocks.py

@@ -896,7 +896,7 @@ def plot_images(self, epoch=None, logs=None, num_rows=1, num_cols=4, reseed=None
 
     def plot_reconstructions(
         self, batch, diffusion_amount=0,
-        epoch=None, logs=None, num_rows=4, num_cols=4,
+        epoch=None, logs=None, num_rows=4, num_cols=4, prefix='./figures/',
     ):
         images = batch[0][self.input_map.input_name()]
         self.normalizer.update_state(images)
@@ -921,7 +921,11 @@ def plot_reconstructions(
                 plt.imshow(generated_images[index], cmap='gray')
                 plt.axis("off")
         plt.tight_layout()
-        plt.show()
+        now_string = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')
+        figure_path = os.path.join(prefix, f'diffusion_image_generations_{now_string}{IMAGE_EXT}')
+        if not os.path.exists(os.path.dirname(figure_path)):
+            os.makedirs(os.path.dirname(figure_path))
+        plt.savefig(figure_path, bbox_inches="tight")
         plt.close()
 
     def control_plot_images(

ml4h/models/train.py

@@ -375,6 +375,7 @@ def train_diffusion_control_model(args, supervised=False):
     if args.inspect_model:
         data, labels, paths = big_batch_from_minibatch_generator(generate_test, 1)
         predictions_to_pngs(data, args.tensor_maps_in, args.tensor_maps_in, data, labels, paths, '{args.output_folder}/{args.id}/')
+        model.plot_reconstructions(data, prefix=f'{args.output_folder}/{args.id}/')
         interpolate_controlled_generations(model, args.tensor_maps_out, args.tensor_maps_out[0], args.batch_size,
                                            f'{args.output_folder}/{args.id}/')
         if model.input_map.axes() == 2: