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1er resultats experience TF sequentiels
This commit is contained in:
Harle, Antoine (Contracteur)
parent
ddac630548
commit
3cffac9852
2 changed files with 37 additions and 53 deletions
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@ -1,29 +1,5 @@
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from utils import *
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tf_names = [
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## Geometric TF ##
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'Identity',
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'FlipUD',
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'FlipLR',
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'Rotate',
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'TranslateX',
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'TranslateY',
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'ShearX',
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'ShearY',
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast',
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'Color',
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'Brightness',
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'Sharpness',
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'Posterize',
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'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
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#Non fonctionnel
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#'Auto_Contrast', #Pas opti pour des batch (Super lent)
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#'Equalize',
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]
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if __name__ == "__main__":
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#### Comparison ####
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@ -44,15 +20,17 @@ if __name__ == "__main__":
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#plot_compare(filenames=files, fig_name="res/compare")
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## Acc, Time, Epochs = f(n_tf) ##
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fig_name="res/TF_nb_tests_compare"
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fig_name="res/TF_seq_tests_compare"
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inner_its = [0, 10]
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dataug_epoch_starts= [0, -1]
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TF_nb = range(1,14+1)
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dataug_epoch_starts= [0]
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TF_nb = 14 #range(1,14+1)
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N_seq_TF= [1, 2, 3, 4, 6]
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fig, ax = plt.subplots(ncols=3, figsize=(30, 8))
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for in_it in inner_its:
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for dataug in dataug_epoch_starts:
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filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF)-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(n_tf, dataug, in_it) for n_tf in TF_nb]
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#filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF)-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(n_tf, dataug, in_it) for n_tf in TF_nb]
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filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF x {})-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(TF_nb, n_tf, dataug, in_it) for n_tf in N_seq_TF]
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all_data=[]
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#legend=""
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@ -62,7 +40,8 @@ if __name__ == "__main__":
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data = json.load(json_file)
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all_data.append(data)
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n_tf = [len(x["Param_names"]) for x in all_data]
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n_tf = N_seq_TF
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#n_tf = [len(x["Param_names"]) for x in all_data]
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acc = [x["Accuracy"] for x in all_data]
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epochs = [len(x["Log"]) for x in all_data]
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time = [x["Time"][0] for x in all_data]
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@ -761,7 +761,7 @@ if __name__ == "__main__":
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print('-'*9)
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'''
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#### Augmented Model ####
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#'''
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'''
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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#tf_dict = TF.TF_dict
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aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), LeNet(3,10)).to(device)
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@ -770,7 +770,7 @@ if __name__ == "__main__":
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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)
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####
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plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it (SOFT)".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
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plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
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print('-'*9)
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times = [x["time"] for x in log]
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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}
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@ -779,14 +779,15 @@ if __name__ == "__main__":
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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print('-'*9)
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#'''
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## TF number tests ##
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'''
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#### TF number tests ####
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#'''
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res_folder="res/TF_nb_tests/"
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epochs= 200
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inner_its = [0, 10]
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dataug_epoch_starts= [0, -1]
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TF_nb = [14] #range(1,len(TF.TF_dict)+1)
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dataug_epoch_starts= [0]
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TF_nb = [len(TF.TF_dict)] #range(1,len(TF.TF_dict)+1)
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N_seq_TF= [1, 2, 3, 4]
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try:
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os.mkdir(res_folder)
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@ -798,24 +799,28 @@ if __name__ == "__main__":
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print("---Starting inner_it", n_inner_iter,"---")
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for dataug_epoch_start in dataug_epoch_starts:
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print("---Starting dataug", dataug_epoch_start,"---")
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for i in TF_nb:
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keys = list(TF.TF_dict.keys())[0:i]
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ntf_dict = {k: TF.TF_dict[k] for k in keys}
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for n_tf in N_seq_TF:
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print("---Starting N_TF", n_tf,"---")
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for i in TF_nb:
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keys = list(TF.TF_dict.keys())[0:i]
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ntf_dict = {k: TF.TF_dict[k] for k in keys}
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aug_model = Augmented_model(Data_augV4(TF_dict=ntf_dict, mix_dist=0.0), LeNet(3,10)).to(device)
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print(str(aug_model), 'on', device_name)
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#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
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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)
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aug_model = Augmented_model(Data_augV4(TF_dict=ntf_dict, N_TF=n_tf, mix_dist=0.0), LeNet(3,10)).to(device)
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print(str(aug_model), 'on', device_name)
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#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
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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)
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####
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plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
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print('-'*9)
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times = [x["time"] for x in log]
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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}
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print(str(aug_model),": acc", out["Accuracy"], "in (ms):", out["Time"][0], "+/-", out["Time"][1])
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with open(res_folder+"log/%s.json" % "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), "w+") as f:
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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print('-'*9)
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####
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plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
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print('-'*9)
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times = [x["time"] for x in log]
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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}
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print(str(aug_model),": acc", out["Accuracy"], "in (ms):", out["Time"][0], "+/-", out["Time"][1])
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with open(res_folder+"log/%s.json" % "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), "w+") as f:
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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print('-'*9)
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'''
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#'''
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