Ajout outil visualisation resultats TF

higher/compare_res.py

@@ -1,5 +1,29 @@
 from utils import *
 
+tf_names = [
+    ## Geometric TF ##
+    'Identity',
+    'FlipUD',
+    'FlipLR',
+    'Rotate',
+    'TranslateX',
+    'TranslateY',
+    'ShearX',
+    'ShearY',
+
+    ## Color TF (Expect image in the range of [0, 1]) ##
+    'Contrast',
+    'Color',
+    'Brightness',
+    'Sharpness',
+    'Posterize',
+    'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
+
+    #Non fonctionnel
+    #'Auto_Contrast', #Pas opti pour des batch (Super lent)
+    #'Equalize',
+]
+
 if __name__ == "__main__":
 
     #### Comparison ####
@@ -21,9 +45,9 @@ if __name__ == "__main__":
 
     ## Acc, Time, Epochs = f(n_tf) ##
     fig_name="res/TF_nb_tests_compare"
-    inner_its = [0, 10]
-    dataug_epoch_starts= [0, -1]
-    TF_nb = range(1,14+1)
+    inner_its = [10]
+    dataug_epoch_starts= [0]
+    TF_nb = [14]#range(1,14+1)
 
     fig, ax = plt.subplots(ncols=3, figsize=(30, 8))
     for in_it in inner_its:
@@ -48,9 +72,11 @@ if __name__ == "__main__":
             ax[1].plot(n_tf, time, label="{} in_it/{} dataug".format(in_it,dataug))
             ax[2].plot(n_tf, epochs, label="{} in_it/{} dataug".format(in_it,dataug))
 
+
             #for data in all_data:
                 #print(np.mean([x["param"] for x in data["Log"]], axis=0))
-            #    print(len(data["Param_names"]), np.argsort(np.argsort(np.mean([x["param"] for x in data["Log"]], axis=0))))
+                #print(len(data["Param_names"]), np.argsort(np.argsort(np.mean([x["param"] for x in data["Log"]], axis=0))))
+
 
     ax[0].set_title('Acc')
     ax[1].set_title('Time')

higher/test_dataug.py

@@ -734,7 +734,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
 ##########################################
 if __name__ == "__main__":
 
-    n_inner_iter = 10
+    n_inner_iter = 0
     epochs = 100
     dataug_epoch_start=0
 
@@ -764,7 +764,7 @@ if __name__ == "__main__":
     #'''
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     #tf_dict = TF.TF_dict
-    aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=1, mix_dist=0.0), LeNet(3,10)).to(device)
+    aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), LeNet(3,10)).to(device)
     print(str(aug_model), 'on', device_name)
     #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
     log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=10)

higher/utils.py

@@ -112,6 +112,16 @@ def plot_res_compare(filenames, fig_name='res'):
     plt.savefig(fig_name, bbox_inches='tight')
     plt.close()
 
+def plot_TF_res(log, tf_names, fig_name='res'):
+
+    fig, ax = plt.subplots(1, 1, figsize=(30, 8), sharey=True)
+    ax.bar(tf_names, np.mean([x["param"] for x in log], axis=0), yerr=np.std([x["param"] for x in log], axis=0))
+    #ax.bar(tf_names, log[-1]["param"])
+
+    fig_name = fig_name.replace('.',',')
+    plt.savefig(fig_name, bbox_inches='tight')
+    plt.close()
+
 def viz_sample_data(imgs, labels, fig_name='data_sample'):
 
     sample = imgs[0:25,].permute(0, 2, 3, 1).squeeze().cpu()