mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
185 lines
7 KiB
Python
185 lines
7 KiB
Python
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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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import torchvision.models as models
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# Use available TF (see transformations.py)
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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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## Bad Tranformations ##
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# Bad Geometric TF #
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#'BShearX',
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#'BShearY',
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#'BTranslateX-',
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#'BTranslateX-',
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#'BTranslateY',
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#'BTranslateY-',
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#'BadContrast',
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#'BadBrightness',
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#'Random',
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#'RandBlend'
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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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torch.backends.cudnn.benchmark = True #Faster if same input size #Not recommended for reproductibility
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#Increase reproductibility
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torch.manual_seed(0)
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np.random.seed(0)
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##########################################
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if __name__ == "__main__":
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n_inner_iter = 1
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epochs = 150
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dataug_epoch_start=0
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optim_param={
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'Meta':{
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'optim':'Adam',
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'lr':1e-2, #1e-2
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},
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'Inner':{
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'optim': 'SGD',
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'lr':1e-1, #1e-2
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'momentum':0.9, #0.9
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}
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}
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model=models.resnet18()
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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####
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'''
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t0 = time.process_time()
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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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log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=True, loss_patience=None)
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exec_time=time.process_time() - t0
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####
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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), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
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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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'''
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####
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'''
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t0 = time.process_time()
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), 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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log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=True, loss_patience=None)
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exec_time=time.process_time() - t0
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####
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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), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
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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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'''
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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= [4, 3, 2]
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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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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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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)) 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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model = ResNet(num_classes=10)
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model = Higher_model(model) #run_dist_dataugV3
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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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print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
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log= run_dist_dataugV3(model=aug_model,
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epochs=epochs,
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inner_it=n_inner_iter,
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dataug_epoch_start=dataug_epoch_start,
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opt_param=optim_param,
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print_freq=50,
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KLdiv=True)
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exec_time=time.process_time() - t0
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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), 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, run)
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with open("../res/log/%s.json" % filename, "w+") as f:
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try:
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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except:
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print("Failed to save logs :",f.name)
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try:
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plot_resV2(log, fig_name="../res/"+filename, param_names=aug_model.TF_names())
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except:
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print("Failed to plot res")
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print('Execution Time : %.00f '%(exec_time))
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print('-'*9)
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#'''
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