mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
93 lines
No EOL
3.9 KiB
Python
93 lines
No EOL
3.9 KiB
Python
from utils import *
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if __name__ == "__main__":
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#'''
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files=[
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#"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json",
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"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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]
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for idx, file in enumerate(files):
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#legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
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#plot_TF_influence(data['Log'], param_names=data['Param_names'])
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#'''
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## Loss , Acc, Proba = f(epoch) ##
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#plot_compare(filenames=files, fig_name="res/compare")
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'''
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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]
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TF_nb = 14#[len(TF.TF_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
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N_seq_TF= [1, 2, 3, 4, 6] #[1]
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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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#n_tf = 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(n_tf, 1, dataug, in_it) for n_tf in TF_nb]
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n_tf = N_seq_TF
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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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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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for idx, file in enumerate(filenames):
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#legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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all_data.append(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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#for i in range(len(time)): time[i] *= epochs[i] #Estimation temps total
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ax[0].plot(n_tf, acc, label="{} in_it/{} dataug".format(in_it,dataug))
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ax[1].plot(n_tf, time, label="{} in_it/{} dataug".format(in_it,dataug))
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ax[2].plot(n_tf, epochs, label="{} in_it/{} dataug".format(in_it,dataug))
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#for data in all_data:
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#print(np.mean([x["param"] for x in data["Log"]], axis=0))
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#print(len(data["Param_names"]), np.argsort(np.argsort(np.mean([x["param"] for x in data["Log"]], axis=0))))
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ax[0].set_title('Acc')
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ax[1].set_title('Time')
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ax[2].set_title('Epochs')
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for a in ax: a.legend()
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fig_name = fig_name.replace('.',',')
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plt.savefig(fig_name, bbox_inches='tight')
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plt.close()
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'''
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#Res print
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'''
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nb_run=3
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accs = []
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times = []
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files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Mix1.0-14TFx2-Mag)-LeNet)-150epochs(dataug:0)-1in_it-%s.json"%str(run) for run in range(nb_run)]
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for idx, file in enumerate(files):
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#legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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accs.append(data['Accuracy'])
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times.append(data['Time'][0])
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print(files[0], np.mean(accs), np.std(accs), np.mean(times))
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''' |