update rec

app.py

@@ -206,6 +206,9 @@ def ncut_run(
     recursion_l2_n_eigs=50,
     recursion_l3_n_eigs=20,
     recursion_metric="euclidean",
+    recursion_l1_gamma=0.5,
+    recursion_l2_gamma=0.5,
+    recursion_l3_gamma=0.5,
     video_output=False,
 ):
     logging_str = ""
@@ -395,6 +398,9 @@ def run_fn(
     recursion_l2_n_eigs=50,
     recursion_l3_n_eigs=20,
     recursion_metric="euclidean",
+    recursion_l1_gamma=0.5,
+    recursion_l2_gamma=0.5,
+    recursion_l3_gamma=0.5,
 ):
     
     if images is None:
@@ -440,6 +446,9 @@ def run_fn(
         "recursion_l2_n_eigs": recursion_l2_n_eigs,
         "recursion_l3_n_eigs": recursion_l3_n_eigs,
         "recursion_metric": recursion_metric,
+        "recursion_l1_gamma": recursion_l1_gamma,
+        "recursion_l2_gamma": recursion_l2_gamma,
+        "recursion_l3_gamma": recursion_l3_gamma,
         "video_output": video_output,
     }
     # print(kwargs)
@@ -596,8 +605,7 @@ def make_output_images_section():
 
 def make_parameters_section():
     gr.Markdown("### Parameters <a style='color: #0044CC;' href='https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/' target='_blank'>Help</a>")
-    # from ncut_pytorch.backbone import list_models, get_demo_model_names
-    from backbone import list_models, get_demo_model_names
+    from ncut_pytorch.backbone import list_models, get_demo_model_names
     model_names = list_models()
     model_names = sorted(model_names)
     model_dropdown = gr.Dropdown(model_names, label="Backbone", value="DiNO(dino_vitb8_448)", elem_id="model_name")
@@ -608,7 +616,7 @@ def make_parameters_section():
     def change_layer_slider(model_name):
         # SD2, UNET
         if "stable" in model_name.lower() and "diffusion" in model_name.lower():
-            from backbone import SD_KEY_DICT
+            from ncut_pytorch.backbone import SD_KEY_DICT
             default_layer = 'up_2_resnets_1_block' if 'diffusion-3' not in model_name else 'block_23'
             return (gr.Slider(1, 49, step=1, label="Diffusion: Timestep (Noise)", value=5, elem_id="layer", visible=True, info="Noise level, 50 is max noise"),
                     gr.Dropdown(SD_KEY_DICT[model_name], label="Diffusion: Layer and Node", value=default_layer, elem_id="node_type", info="From the SD U-Net"))
@@ -760,7 +768,9 @@ with demo:
                     l2_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #2: N eigenvectors", value=50, elem_id="l2_num_eig")
                     l3_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #3: N eigenvectors", value=50, elem_id="l3_num_eig")
                     metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="Recursion distance metric", value="cosine", elem_id="recursion_metric")
-                    
+                    l1_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #1: Affinity focal gamma", value=0.5, elem_id="recursion_l1_gamma")
+                    l2_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #2: Affinity focal gamma", value=0.5, elem_id="recursion_l2_gamma")
+                    l3_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #3: Affinity focal gamma", value=0.5, elem_id="recursion_l3_gamma")
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                     affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
@@ -769,6 +779,7 @@ with demo:
                     sampling_method_dropdown
                 ] = make_parameters_section()
                 num_eig_slider.visible = False
+                affinity_focal_gamma_slider.visible = False
                 # logging text box
         with gr.Row():
             with gr.Column(scale=5, min_width=200):
@@ -802,7 +813,8 @@ with demo:
                 embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                 perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown,
                 false_placeholder, number_placeholder, true_placeholder,
-                l2_num_eig_slider, l3_num_eig_slider, metric_dropdown,
+                l2_num_eig_slider, l3_num_eig_slider, metric_dropdown, 
+                l1_affinity_focal_gamma_slider, l2_affinity_focal_gamma_slider, l3_affinity_focal_gamma_slider
             ],
             outputs=[l1_gallery, l2_gallery, l3_gallery, logging_text],
             api_name="API_RecursiveCut"