Augmented Dataset fonctionnel

higher/test_brutus.py

@@ -0,0 +1,83 @@
+from model import *
+from dataug import *
+#from utils import *
+from train_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
+]
+
+device = torch.device('cuda')
+
+if device == torch.device('cpu'):
+    device_name = 'CPU'
+else:
+    device_name = torch.cuda.get_device_name(device)
+
+##########################################
+if __name__ == "__main__":
+
+    res_folder="res/brutus-tests/"
+    epochs= 150
+    inner_its = [1]
+    dist_mix = [1]
+    dataug_epoch_starts= [0]
+    tf_dict = {k: TF.TF_dict[k] for k in tf_names}
+    TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
+    N_seq_TF= [2, 3, 4]
+    mag_setup = [(True,True), (False, False)]
+    #prob_setup = [True, False]
+    nb_run= 3
+    
+    try:
+        os.mkdir(res_folder)
+        os.mkdir(res_folder+"log/")
+    except FileExistsError:
+        pass
+
+    for n_inner_iter in inner_its:
+        for dataug_epoch_start in dataug_epoch_starts:
+            for n_tf in N_seq_TF:
+                for dist in dist_mix:
+                    #for i in TF_nb:
+                    for m_setup in mag_setup:
+                        #for p_setup in prob_setup:
+                        for run in range(nb_run):
+                            if n_inner_iter == 0 and (m_setup!=(True,True) or p_setup!=True): continue #Autres setup inutiles sans meta-opti
+                            if n_tf ==2 and m_setup==(True,True): continue #Deja resultats
+                            #keys = list(TF.TF_dict.keys())[0:i]
+                            #ntf_dict = {k: TF.TF_dict[k] for k in keys}
+
+                            aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=False, fixed_mag=m_setup[0], shared_mag=m_setup[1]), 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=20, loss_patience=None)
+
+                            ####
+                            print('-'*9)
+                            times = [x["time"] for x in log]
+                            out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
+                            print(str(aug_model),": acc", out["Accuracy"], "in :", out["Time"][0], "+/-", out["Time"][1])
+                            filename = "{}-{}epochs(dataug:{})-{}in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter,run)
+                            with open(res_folder+"log/%s.json" % filename, "w+") as f:
+                                json.dump(out, f, indent=True)
+                                print('Log :\"',f.name, '\" saved !')
+
+                            #plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
+                            print('-'*9)