add preset selection dropdown, fix incorrect x2z and json not updated

app.py

@@ -47,6 +47,8 @@ CONFIG_PATH = "presets/rt_config.yaml"
 PCA_PARAM_FILE = "presets/internal/gaussian.npz"
 INFO_PATH = "presets/internal/info.json"
 MASK_PATH = "presets/internal/feature_mask.npy"
+PRESET_PATH = "presets/internal/raw_params.npy"
+TRAIN_INDEX_PATH = "presets/internal/train_index.npy"
 
 
 with open(CONFIG_PATH) as fp:
@@ -56,16 +58,21 @@ with open(CONFIG_PATH) as fp:
 global_fx = instantiate(fx_config)
 global_fx.eval()
 
+raw_params = torch.from_numpy(np.load(PRESET_PATH))
+train_index = torch.from_numpy(np.load(TRAIN_INDEX_PATH))
+feature_mask = torch.from_numpy(np.load(MASK_PATH))
+presets = raw_params[train_index][:, feature_mask].contiguous()
+
 pca_params = np.load(PCA_PARAM_FILE)
 mean = pca_params["mean"]
 cov = pca_params["cov"]
 eigvals, eigvecs = np.linalg.eigh(cov)
 eigvals = np.flip(eigvals, axis=0)
 eigvecs = np.flip(eigvecs, axis=1)
-U = eigvecs * np.sqrt(eigvals)
-U = torch.from_numpy(U).float()
+eigsqrt = torch.from_numpy(eigvals.copy()).float().sqrt()
+U = torch.from_numpy(eigvecs.copy()).float()
 mean = torch.from_numpy(mean).float()
-feature_mask = torch.from_numpy(np.load(MASK_PATH))
+
 # Global latent variable
 # z = torch.zeros_like(mean)
 
@@ -104,7 +111,7 @@ meter = pyln.Meter(44100)
 def z2x(z):
     # close all figures to avoid too many open figures
     plt.close("all")
-    x = U @ z + mean
+    x = U @ (z * eigsqrt) + mean
     # # print(z)
     # fx.load_state_dict(vec2dict(x), strict=False)
     # fx.apply(partial(clip_delay_eq_Q, Q=0.707))
@@ -123,7 +130,7 @@ def fx2x(fx):
 @torch.no_grad()
 def x2z(x):
     z = U.T @ (x - mean)
-    return z
+    return z / eigsqrt
 
 
 @torch.no_grad()
@@ -393,13 +400,23 @@ with gr.Blocks() as demo:
 
             sliders = [s1, s2, s3, s4]
 
-            extra_pc_dropdown = gr.Dropdown(
-                list(range(NUMBER_OF_PCS + 1, mean.numel() + 1)),
-                label=f"PC > {NUMBER_OF_PCS}",
-                info="Select which extra PC to adjust",
-                interactive=True,
-            )
-            extra_slider = default_pc_slider(label="Extra PC")
+            with gr.Row():
+                with gr.Column():
+                    extra_pc_dropdown = gr.Dropdown(
+                        list(range(NUMBER_OF_PCS + 1, mean.numel() + 1)),
+                        label=f"PC > {NUMBER_OF_PCS}",
+                        info="Select which extra PC to adjust",
+                        interactive=True,
+                    )
+                    extra_slider = default_pc_slider(label="Extra PC")
+
+                preset_dropdown = gr.Dropdown(
+                    ["none"] + list(range(1, presets.shape[0] + 1)),
+                    value="none",
+                    label=f"Select Preset (1-{presets.shape[0]})",
+                    info="Select a preset to load (this will override the current settings)",
+                    interactive=True,
+                )
 
         with gr.Column():
             audio_output = default_audio_block(label="Output Audio", interactive=False)
@@ -853,8 +870,15 @@ with gr.Blocks() as demo:
         t60_plot,
     ]
 
-    update_all = lambda z, fx, i: update_pc(z, i) + update_fx(fx) + update_plots(fx)
-    update_all_outputs = update_pc_outputs + update_fx_outputs + update_plots_outputs
+    update_all = (
+        lambda z, fx, i: update_pc(z, i)
+        + update_fx(fx)
+        + update_plots(fx)
+        + [model2json(fx)]
+    )
+    update_all_outputs = (
+        update_pc_outputs + update_fx_outputs + update_plots_outputs + [json_output]
+    )
 
     random_button.click(
         chain_functions(
@@ -912,4 +936,14 @@ with gr.Blocks() as demo:
         outputs=extra_slider,
     )
 
+    preset_dropdown.input(
+        chain_functions(
+            lambda i, _: (mean if i == "none" else presets[i - 1], _),
+            lambda x, i: (x2z(x), x, vec2fx(x), i),
+            lambda z, x, fx, i: [z, x] + update_all(z, fx, i),
+        ),
+        inputs=[preset_dropdown, extra_pc_dropdown],
+        outputs=[z, fx_params] + update_all_outputs,
+    )
+
 demo.launch()