mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 04:00:46 +02:00
170 lines
6.4 KiB
Python
170 lines
6.4 KiB
Python
from model import *
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from dataug import *
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#from utils import *
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from train_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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#Color TF (Common mag scale)
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#'+Contrast',
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#'+Color',
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#'+Brightness',
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#'+Sharpness',
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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',
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#'BRotate',
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#'BTranslateX',
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#'BTranslateY',
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#'BShearX',
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#'BShearY',
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#'BadTranslateX',
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#'BadTranslateX_neg',
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#'BadTranslateY',
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#'BadTranslateY_neg',
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#'BadColor',
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#'BadSharpness',
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#'BadContrast',
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#'BadBrightness',
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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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device = torch.device('cuda')
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if device == torch.device('cpu'):
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device_name = 'CPU'
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else:
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device_name = torch.cuda.get_device_name(device)
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##########################################
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if __name__ == "__main__":
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n_inner_iter = 0
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epochs = 150
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dataug_epoch_start=0
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#### Classic ####
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'''
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model = LeNet(3,10).to(device)
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#model = WideResNet(num_classes=10, wrn_size=16).to(device)
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#model = Augmented_model(Data_augV3(mix_dist=0.0), LeNet(3,10)).to(device)
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#model.augment(mode=False)
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print(str(model), 'on', device_name)
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log= train_classic(model=model, epochs=epochs)
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#log= train_classic_higher(model=model, epochs=epochs)
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####
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plot_res(log, fig_name="res/{}-{} epochs".format(str(model),epochs))
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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, "Log": log}
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print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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with open("res/log/%s.json" % "{}-{} epochs".format(str(model),epochs), "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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#### Augmented Model ####
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#'''
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t0 = time.process_time()
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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_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
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#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
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aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), 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=None)
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####
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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:", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
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with open("res/log/%s.json" % filename, "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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plot_resV2(log, fig_name="res/"+filename, param_names=tf_names)
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print('Execution Time : %.00f '%(time.process_time() - t0))
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print('-'*9)
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#'''
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#### TF tests ####
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'''
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res_folder="res/brutus-tests/"
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epochs= 150
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inner_its = [1, 10]
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dist_mix = [0.0, 0.5, 1]
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dataug_epoch_starts= [0]
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
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N_seq_TF= [1, 2, 3, 4]
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mag_setup = [(True,True), (False, False)]
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#prob_setup = [True, False]
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nb_run= 3
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try:
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os.mkdir(res_folder)
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os.mkdir(res_folder+"log/")
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except FileExistsError:
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pass
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for n_inner_iter in inner_its:
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for dataug_epoch_start in dataug_epoch_starts:
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for n_tf in N_seq_TF:
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for dist in dist_mix:
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#for i in TF_nb:
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for m_setup in mag_setup:
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#for p_setup in prob_setup:
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for run in range(nb_run):
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if n_inner_iter == 0 and (m_setup!=(True,True) or p_setup!=True): continue #Autres setup inutiles sans meta-opti
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if n_inner_iter ==1 and (n_tf==1 or n_tf==2): continue #Deja resultats
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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_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)
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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=20, loss_patience=None)
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####
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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 :", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{}epochs(dataug:{})-{}in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter,run)
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with open(res_folder+"log/%s.json" % filename, "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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#plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
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print('-'*9)
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'''
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