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https://github.com/AntoineHX/smart_augmentation.git
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RandAugment
This commit is contained in:
Harle, Antoine (Contracteur)
parent
7221142a9a
commit
6277e268c1
3 changed files with 407 additions and 73 deletions
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@ -1,3 +1,6 @@
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""" Script to run series of experiments.
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"""
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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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@ -13,14 +16,16 @@ optim_param={
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},
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'Inner':{
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'optim': 'SGD',
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'lr':1e-1, #1e-2 #1e-1 for ResNet
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'lr':1e-2, #1e-2 #1e-1 for ResNet
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'momentum':0.9, #0.9
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}
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}
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res_folder="../res/benchmark/CIFAR10/"
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epochs= 150
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#res_folder="../res/HPsearch/"
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epochs= 200
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dataug_epoch_start=0
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nb_run= 3
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# Use available TF (see transformations.py)
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tf_names = [
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@ -80,60 +85,107 @@ if __name__ == "__main__":
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'''
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for model_type in model_list.keys():
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for model_name in model_list[model_type]:
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model = getattr(model_type, model_name)(pretrained=False)
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for run in range(nb_run):
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t0 = time.process_time()
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torch.cuda.reset_max_memory_cached() #reset_peak_stats
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t0 = time.perf_counter()
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model = Higher_model(model) #run_dist_dataugV3
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if n_inner_iter!=0:
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aug_model = Augmented_model(
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Data_augV5(TF_dict=tf_dict,
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N_TF=n_tf,
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mix_dist=dist,
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fixed_prob=p_setup,
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fixed_mag=m_setup[0],
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shared_mag=m_setup[1]),
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model).to(device)
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else:
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aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=n_tf), model).to(device)
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model = getattr(model_type, model_name)(pretrained=False)
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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=epochs/4,
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unsup_loss=1,
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hp_opt=False,
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save_sample_freq=None)
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model = Higher_model(model, model_name) #run_dist_dataugV3
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if n_inner_iter!=0:
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aug_model = Augmented_model(
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Data_augV5(TF_dict=tf_dict,
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N_TF=n_tf,
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mix_dist=dist,
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fixed_prob=p_setup,
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fixed_mag=m_setup[0],
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shared_mag=m_setup[1]),
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model).to(device)
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else:
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aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=n_tf), model).to(device)
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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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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=epochs/4,
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unsup_loss=1,
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hp_opt=False,
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save_sample_freq=None)
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print('Execution Time : %.00f '%(exec_time))
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print('-'*9)
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exec_time=time.perf_counter() - t0
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max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
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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]),
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"Time": (np.mean(times),np.std(times), exec_time),
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'Optimizer': optim_param,
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"Device": device_name,
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"Memory": max_cached,
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"Param_names": aug_model.TF_names(),
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"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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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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print('Execution Time : %.00f '%(exec_time))
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print('-'*9)
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'''
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### Benchmark - RandAugment ###
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for model_type in model_list.keys():
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for model_name in model_list[model_type]:
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for run in range(nb_run):
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torch.cuda.reset_max_memory_cached() #reset_peak_stats
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t0 = time.perf_counter()
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model = getattr(model_type, model_name)(pretrained=False).to(device)
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print("RandAugment(N{}-M{})-{} on {} for {} epochs".format(rand_aug['N'],rand_aug['M'],model_name, device_name, epochs))
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log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=epochs/4)
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exec_time=time.perf_counter() - t0
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max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
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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]),
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"Time": (np.mean(times),np.std(times), exec_time),
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'Optimizer': optim_param,
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"Device": device_name,
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"Memory": max_cached,
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"Rand_Aug": rand_aug,
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"Log": log}
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print("RandAugment-",model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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filename = "RandAugment(N{}-M{})-{}-{} epochs -{}".format(rand_aug['N'],rand_aug['M'],model_name,epochs, run)
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with open(res_folder+"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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#plot_resV2(log, fig_name=res_folder+filename)
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print('Execution Time : %.00f '%(exec_time))
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print('-'*9)
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### HP Search ###
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'''
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from LeNet import *
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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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N_seq_TF= [2, 3, 4]
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N_seq_TF= [3, 2, 4]
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mag_setup = [(True,True), (False, False)] #(FxSh, Independant)
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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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@ -150,9 +202,10 @@ if __name__ == "__main__":
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p_setup=False
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for run in range(nb_run):
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t0 = time.process_time()
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t0 = time.perf_counter()
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model = getattr(models.resnet, 'resnet18')(pretrained=False)
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#model = getattr(models.resnet, 'resnet18')(pretrained=False)
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model = LeNet(3,10)
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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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@ -168,7 +221,7 @@ if __name__ == "__main__":
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hp_opt=False,
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save_sample_freq=None)
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exec_time=time.process_time() - t0
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exec_time=time.perf_counter() - 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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@ -184,4 +237,4 @@ if __name__ == "__main__":
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print('Execution Time : %.00f '%(exec_time))
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print('-'*9)
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#'''
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'''
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""" Dataset definition.
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MNIST / CIFAR10
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MNIST / CIFAR10 / CIFAR100 / SVHN / ImageNet
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"""
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import torch
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from torch.utils.data.dataset import ConcatDataset
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@ -37,9 +37,16 @@ transform_train = torchvision.transforms.Compose([
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#transforms.RandomVerticalFlip(),
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torchvision.transforms.ToTensor(),
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])
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#from RandAugment import RandAugment
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## RandAugment ##
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from RandAugment import RandAugment
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# Add RandAugment with N, M(hyperparameter)
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#transform_train.transforms.insert(0, RandAugment(n=2, m=30))
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rand_aug={'N': 2, 'M': 1}
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#rand_aug={'N': 2, 'M': 9./30} #RN-ImageNet
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#rand_aug={'N': 3, 'M': 5./30} #WRN-CIFAR10
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#rand_aug={'N': 2, 'M': 14./30} #WRN-CIFAR100
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#rand_aug={'N': 3, 'M': 7./30} #WRN-SVHN
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transform_train.transforms.insert(0, RandAugment(n=rand_aug['N'], m=rand_aug['M']))
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### Classic Dataset ###
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@ -50,7 +57,7 @@ transform_train = torchvision.transforms.Compose([
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#CIFAR
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data_train = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform_train)
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#data_val = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform)
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data_val = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform)
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data_test = torchvision.datasets.CIFAR10(dataroot, train=False, download=download_data, transform=transform)
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#data_train = torchvision.datasets.CIFAR100(dataroot, train=True, download=download_data, transform=transform_train)
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@ -72,32 +79,18 @@ data_test = torchvision.datasets.CIFAR10(dataroot, train=False, download=downloa
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#Validation set size [0, 1]
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valid_size=0.1
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#train_subset_indices=range(int(len(data_train)*(1-valid_size)))
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#val_subset_indices=range(int(len(data_train)*(1-valid_size)),len(data_train))
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train_subset_indices=range(int(len(data_train)*(1-valid_size)))
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val_subset_indices=range(int(len(data_train)*(1-valid_size)),len(data_train))
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#train_subset_indices=range(BATCH_SIZE*10)
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#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
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#from torch.utils.data import SubsetRandomSampler
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#dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
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#dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
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from torch.utils.data import SubsetRandomSampler
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dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
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dl_val = torch.utils.data.DataLoader(data_val, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
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dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
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#Cross Validation
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'''
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from skorch.dataset import CVSplit
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import numpy as np
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cvs = CVSplit(cv=valid_size, stratified=True) #Stratified =True for unbalanced dataset #ShuffleSplit
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def next_CVSplit():
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train_subset, val_subset = cvs(data_train, y=np.array(data_train.targets))
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dl_train = torch.utils.data.DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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dl_val = torch.utils.data.DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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return dl_train, dl_val
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dl_train, dl_val = next_CVSplit()
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'''
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import numpy as np
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from sklearn.model_selection import ShuffleSplit
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from sklearn.model_selection import StratifiedShuffleSplit
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@ -134,7 +127,7 @@ class CVSplit(object):
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else:
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cv_cls = ShuffleSplit
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self._cv= cv_cls(test_size=val_size, random_state=0)
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self._cv= cv_cls(test_size=val_size, random_state=0) #Random state w/ fixed seed
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def next_split(self):
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""" Get next cross-validation split.
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@ -157,4 +150,21 @@ class CVSplit(object):
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return dl_train, dl_val
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cvs = CVSplit(data_train, val_size=valid_size)
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dl_train, dl_val = cvs.next_split()
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dl_train, dl_val = cvs.next_split()
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'''
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'''
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from skorch.dataset import CVSplit
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import numpy as np
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cvs = CVSplit(cv=valid_size, stratified=True) #Stratified =True for unbalanced dataset #ShuffleSplit
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def next_CVSplit():
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train_subset, val_subset = cvs(data_train, y=np.array(data_train.targets))
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dl_train = torch.utils.data.DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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dl_val = torch.utils.data.DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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return dl_train, dl_val
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dl_train, dl_val = next_CVSplit()
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'''
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