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https://github.com/AntoineHX/smart_augmentation.git
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Brutus
This commit is contained in:
Harle, Antoine (Contracteur)
parent
53bd421670
commit
e291bc2e44
9 changed files with 55 additions and 44 deletions
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@ -2,13 +2,12 @@ from utils import *
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if __name__ == "__main__":
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'''
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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(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-0.json",
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"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-1.json",
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"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.json",
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"res/log/Aug_mod(Data_augV5(Mix0.8-23TFx4-Mag)-LeNet)-100 epochs (dataug:0)- 1 in_it.json",
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#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-0.json",
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#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-1.json",
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#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.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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@ -18,7 +17,7 @@ if __name__ == "__main__":
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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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#'''
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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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@ -78,7 +77,7 @@ if __name__ == "__main__":
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'''
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#Res print
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#'''
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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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@ -93,4 +92,4 @@ if __name__ == "__main__":
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print(idx, data['Accuracy'])
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print(files[0], np.mean(accs), np.std(accs), np.mean(times))
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#'''
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'''
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@ -531,7 +531,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
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return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF)
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class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True):
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True, ):
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super(Data_augV5, self).__init__()
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assert len(TF_dict)>0
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@ -548,8 +548,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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#self._fixed_mag=5 #[0, PARAMETER_MAX]
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self._params = nn.ParameterDict({
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"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
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"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)) if self._shared_mag
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else torch.tensor(float(TF.PARAMETER_MAX)).expand(self._nb_tf)), #[0, PARAMETER_MAX]
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"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)/2) if self._shared_mag
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else torch.tensor(float(TF.PARAMETER_MAX)/2).expand(self._nb_tf)), #[0, PARAMETER_MAX]
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})
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#for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag
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@ -633,7 +633,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
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if not self._fixed_mag:
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self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX) #Bloque une fois au extreme
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self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX)
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#self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
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def loss_weight(self):
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@ -93,15 +93,15 @@ 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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'''
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res_folder="res/brutus-tests/"
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res_folder="res/brutus-tests2/"
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epochs= 150
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inner_its = [1]
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dist_mix = [0.0, 0.5, 0.8, 1.0]
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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= [2, 3]
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mag_setup = [(True,True), (False, False)]
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N_seq_TF= [2, 3, 4]
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mag_setup = [(True,True), (False, False)] #(Fixed, Shared)
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#prob_setup = [True, False]
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nb_run= 3
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@ -118,12 +118,14 @@ if __name__ == "__main__":
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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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p_setup=True
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p_setup=False
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for run in range(nb_run):
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if n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True): continue #Autres setup inutiles sans meta-opti
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if (n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True)) or (p_setup and dist!=0.0): continue #Autres setup inutiles sans meta-opti
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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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t0 = time.process_time()
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=p_setup, fixed_mag=m_setup[0], shared_mag=m_setup[1]), model).to(device)
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#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
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@ -143,9 +145,9 @@ if __name__ == "__main__":
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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), exec_time), 'Optimizer': optim_param, "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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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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print('-'*9)
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'''
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#'''
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@ -19,8 +19,8 @@ tf_names = [
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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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#'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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@ -66,7 +66,7 @@ if __name__ == "__main__":
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#'aug_dataset',
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'aug_model'
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}
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n_inner_iter = 10
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n_inner_iter = 1
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epochs = 100
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dataug_epoch_start=0
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optim_param={
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@ -168,7 +168,7 @@ if __name__ == "__main__":
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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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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)
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.8, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
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#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
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print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
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@ -187,7 +187,7 @@ if __name__ == "__main__":
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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), exec_time), 'Optimizer': optim_param, "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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filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)+"demi_mag"
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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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@ -90,6 +90,7 @@ def plot_resV2(log, fig_name='res', param_names=None):
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ax[0, 2].set_title('Mag =f(epoch)')
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ax[0, 2].stackplot(epochs, mag, labels=param_names)
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#ax[0, 2].plot(epochs, np.array(mag).T, label=param_names)
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ax[0, 2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
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ax[1, 2].set_title('Mag =f(TF)')
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Binary file not shown.
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@ -31,12 +31,12 @@ tf_names = [
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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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#'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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]
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class Lambda(nn.Module):
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@ -95,6 +95,7 @@ def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, mas
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unsupp_coeff = 1
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loss = sup_loss + (aug_loss + KL_loss) * unsupp_coeff
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#print(sup_loss.item(), (aug_loss + KL_loss).item())
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optimizer.zero_grad()
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loss.backward()
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@ -210,7 +211,7 @@ def get_train_valid_loader(args, augment, random_seed, valid_size=0.1, shuffle=T
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split = int(np.floor(valid_size * num_train))
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if shuffle:
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#np.random.seed(random_seed)
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np.random.seed(random_seed)
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np.random.shuffle(indices)
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train_idx, valid_idx = indices[split:], indices[:split]
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@ -277,6 +278,8 @@ def main(args):
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model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
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if args.augment=='RandKL': Kldiv=True
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model['data_aug']['mag'].data = model['data_aug']['mag'].data * args.magnitude
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print("Augmodel")
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# model.fc = nn.Linear(model.fc.in_features, 2)
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@ -294,7 +297,7 @@ def main(args):
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optimizer,
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lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9)
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es = utils.EarlyStopping()
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es = utils.EarlyStopping() if not (args.augment=='Rand' or args.augment=='RandKL') else utils.EarlyStopping(augmented_model=True)
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if args.test_only:
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model.load_state_dict(torch.load('checkpoint.pt', map_location=lambda storage, loc: storage))
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@ -324,8 +327,8 @@ def main(args):
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# print('Train')
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# print(train_confmat)
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print('Valid')
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print(valid_confmat)
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#print('Valid')
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#print(valid_confmat)
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# if es.early_stop:
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# break
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@ -339,9 +342,9 @@ def parse_args():
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import argparse
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parser = argparse.ArgumentParser(description='PyTorch Classification Training')
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parser.add_argument('--data-path', default='/Salvador', help='dataset')
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parser.add_argument('--data-path', default='/github/smart_augmentation/salvador/data', help='dataset')
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parser.add_argument('--model', default='resnet18', help='model') #'resnet18'
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parser.add_argument('--device', default='cuda:1', help='device')
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parser.add_argument('--device', default='cuda:0', help='device')
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parser.add_argument('-b', '--batch-size', default=8, type=int)
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parser.add_argument('--epochs', default=3, type=int, metavar='N',
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help='number of total epochs to run')
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@ -364,6 +367,10 @@ def parse_args():
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parser.add_argument('-a', '--augment', default='None', type=str,
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metavar='N', help='Data augment',
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dest='augment')
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parser.add_argument('-m', '--magnitude', default=1.0, type=float,
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metavar='N', help='Augmentation magnitude',
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dest='magnitude')
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args = parser.parse_args()
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@ -549,10 +549,10 @@ def parse_args():
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import argparse
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parser = argparse.ArgumentParser(description='PyTorch Classification Training')
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parser.add_argument('--data-path', default='/Salvador', help='dataset')
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parser.add_argument('--model', default='resnet50', help='model')
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parser.add_argument('--device', default='cuda:1', help='device')
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parser.add_argument('-b', '--batch-size', default=4, type=int)
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parser.add_argument('--data-path', default='/github/smart_augmentation/salvador/data', help='dataset')
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parser.add_argument('--model', default='resnet18', help='model') #'resnet18'
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parser.add_argument('--device', default='cuda:0', help='device')
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parser.add_argument('-b', '--batch-size', default=8, type=int)
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parser.add_argument('--epochs', default=3, type=int, metavar='N',
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help='number of total epochs to run')
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parser.add_argument('-j', '--workers', default=0, type=int, metavar='N',
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@ -157,7 +157,7 @@ def accuracy(output, target, topk=(1,)):
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class EarlyStopping:
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"""Early stops the training if validation loss doesn't improve after a given patience."""
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def __init__(self, patience=7, verbose=False, delta=0):
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def __init__(self, patience=7, verbose=False, delta=0, augmented_model=False):
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"""
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Args:
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patience (int): How long to wait after last time validation loss improved.
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@ -175,6 +175,8 @@ class EarlyStopping:
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self.val_loss_min = np.Inf
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self.delta = delta
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self.augmented_model = augmented_model
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def __call__(self, val_loss, model):
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score = -val_loss
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@ -196,5 +198,5 @@ class EarlyStopping:
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'''Saves model when validation loss decrease.'''
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if self.verbose:
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print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
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torch.save(model.state_dict(), 'checkpoint.pt')
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torch.save(model.state_dict(), 'checkpoint.pt') if not self.augmented_model else torch.save(model['model'].state_dict(), 'checkpoint.pt')
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self.val_loss_min = val_loss
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