From 27c1890f4c83202bf59dcf665b02b9d0d53d8274 Mon Sep 17 00:00:00 2001 From: "Harle, Antoine (Contracteur)" Date: Mon, 13 Jan 2020 10:59:32 -0500 Subject: [PATCH] modification mineurs --- higher/dataug.py | 3 +- higher/test_dataug.py | 64 ++++++++++++++++++++----------------------- higher/utils.py | 2 +- 3 files changed, 32 insertions(+), 37 deletions(-) diff --git a/higher/dataug.py b/higher/dataug.py index 295a007..714ae4c 100755 --- a/higher/dataug.py +++ b/higher/dataug.py @@ -692,7 +692,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba) else: return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param) -class Data_augV6(nn.Module): #Optimisation sequentielle + +class Data_augV6(nn.Module): #Optimisation sequentielle #Mauvais resultats def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=None, fixed_mag=True, shared_mag=True): super(Data_augV6, self).__init__() assert len(TF_dict)>0 diff --git a/higher/test_dataug.py b/higher/test_dataug.py index 7f60822..137eb02 100755 --- a/higher/test_dataug.py +++ b/higher/test_dataug.py @@ -6,21 +6,21 @@ from train_utils import * tf_names = [ ## Geometric TF ## 'Identity', - #'FlipUD', - #'FlipLR', - #'Rotate', - #'TranslateX', - #'TranslateY', - #'ShearX', - #'ShearY', + '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 + 'Contrast', + 'Color', + 'Brightness', + 'Sharpness', + 'Posterize', + 'Solarize', #=>Image entre [0,1] #Pas opti pour des batch #Color TF (Common mag scale) #'+Contrast', @@ -34,23 +34,18 @@ tf_names = [ #'=Posterize', #'=Solarize', - #'BRotate', - #'BTranslateX', - #'BTranslateY', - #'BShearX', - #'BShearY', - #'BadTranslateX', - #'BadTranslateX_neg', - #'BadTranslateY', - #'BadTranslateY_neg', + 'BShearX', + 'BShearY', + 'BTranslateX-', + 'BTranslateX-', + 'BTranslateY', + 'BTranslateY-', - #'BadColor', - #'BadSharpness', - #'BadContrast', - #'BadBrightness', + 'BadContrast', + 'BadBrightness', 'Random', - #'RandBlend' + 'RandBlend' #Non fonctionnel #'Auto_Contrast', #Pas opti pour des batch (Super lent) #'Equalize', @@ -71,8 +66,8 @@ if __name__ == "__main__": #'aug_dataset', 'aug_model' } - n_inner_iter = 1 - epochs = 1 + n_inner_iter = 10 + epochs = 100 dataug_epoch_start=0 optim_param={ 'Meta':{ @@ -155,7 +150,7 @@ if __name__ == "__main__": #### 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), exec_time), 'Optimizer': optim_param['Inner'], "Device": device_name, "Log": log} + out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param['Inner'], "Device": device_name, "Param_names": data_train_aug._TF, "Log": log} print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) filename = "{}-{}-{} epochs".format(str(data_train_aug),str(model),epochs) with open("res/log/%s.json" % filename, "w+") as f: @@ -173,8 +168,7 @@ if __name__ == "__main__": t0 = time.process_time() tf_dict = {k: TF.TF_dict[k] for k in tf_names} - #aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), model).to(device) - aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.5, fixed_prob=False, fixed_mag=True, shared_mag=True), model).to(device) + aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device) #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device) print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter)) @@ -183,7 +177,7 @@ if __name__ == "__main__": inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, opt_param=optim_param, - print_freq=1, + print_freq=10, KLdiv=True, loss_patience=None) @@ -191,14 +185,14 @@ if __name__ == "__main__": #### 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), exec_time), 'Optimizer': optim_param, "Device": device_name, "Log": log} + out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "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) with open("res/log/%s.json" % filename, "w+") as f: json.dump(out, f, indent=True) print('Log :\"',f.name, '\" saved !') - plot_resV2(log, fig_name="res/"+filename, param_names=tf_names) + plot_resV2(log, fig_name="res/"+filename, param_names=aug_model.TF_names()) print('Execution Time : %.00f '%(exec_time)) print('-'*9) \ No newline at end of file diff --git a/higher/utils.py b/higher/utils.py index f7c5ab0..068d2e1 100755 --- a/higher/utils.py +++ b/higher/utils.py @@ -195,7 +195,7 @@ def viz_sample_data(imgs, labels, fig_name='data_sample', weight_labels=None): plt.grid(False) plt.imshow(sample[i,].detach().numpy(), cmap=plt.cm.binary) label = str(labels[i].item()) - if weight_labels is not None : label+= ("- p %.2f" % weight_labels[i].item()) + if weight_labels is not None : label+= (" - p %.2f" % weight_labels[i].item()) plt.xlabel(label) plt.savefig(fig_name)