Add tqdm to the main optimisation loop

main.py

@@ -424,6 +424,7 @@ def show_full_render(
     target_silhouette,
     loss_history_color,
     loss_history_sil,
+    iteration
 ):
     """
     This is a helper function for visualizing the intermediate results of the
@@ -481,10 +482,11 @@ def show_full_render(
             ax_.axis("off")
         ax_.set_title(title_)
     fig.canvas.draw()
-    fig.show()
-    display.clear_output(wait=True)
-    display.display(fig)
-    return fig
+    fig.savefig(f"results/fullrender_{iteration:05d}.png")
+    #fig.show()
+    #display.clear_output(wait=True)
+    #display.display(fig)
+    #return fig
 
 
 ###############################################################################
@@ -521,11 +523,11 @@ n_iter = 3000
 loss_history_color, loss_history_sil = [], []
 
 # The main optimization loop.
-for iteration in range(n_iter):
+for iteration in tqdm(range(n_iter)):
     # In case we reached the last 75% of iterations,
     # decrease the learning rate of the optimizer 10-fold.
     if iteration == round(n_iter * 0.75):
-        print("Decreasing LR 10-fold ...")
+        tqdm.write("Decreasing LR 10-fold ...")
         optimizer = torch.optim.Adam(
             neural_radiance_field.parameters(), lr=lr * 0.1
         )
@@ -595,7 +597,7 @@ for iteration in range(n_iter):
 
     # Every 10 iterations, print the current values of the losses.
     if iteration % 10 == 0:
-        print(
+        tqdm.write(
             f"Iteration {iteration:05d}:"
             + f" loss color = {float(color_err):1.2e}"
             + f" loss silhouette = {float(sil_err):1.2e}"
@@ -623,6 +625,7 @@ for iteration in range(n_iter):
             target_silhouettes[show_idx][0],
             loss_history_color,
             loss_history_sil,
+            iteration,
         )
 
 ###############################################################################