mirror of
https://github.com/AntoineHX/smart_augmentation.git
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232 lines
11 KiB
Python
232 lines
11 KiB
Python
""" 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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from transformations import TF_loader
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import torchvision.models as models
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#model_list={models.resnet: ['resnet18', 'resnet50','wide_resnet50_2']} #lr=0.1
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model_list={models.resnet: ['resnet18']}
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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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'epoch_start': 2, #0 / 2 (Resnet?)
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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 (ResNet)
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'momentum':0.9, #0.9
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'decay':0.0005, #0.0005
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'nesterov':False, #False (True: Bad behavior w/ Data_aug)
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'scheduler':'cosine', #None, 'cosine', 'multiStep', 'exponential'
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}
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}
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res_folder="../res/benchmark/CIFAR10/"
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#res_folder="../res/HPsearch/"
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epochs= 400
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dataug_epoch_start=0
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nb_run= 1
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tf_config='../config/base_tf_config.json'
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TF_loader=TF_loader()
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tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
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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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### Benchmark ###
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#'''
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n_inner_iter = 1#[0, 1]
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dist_mix = [0.5]
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N_seq_TF= [3, 4]
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mag_setup = [(False, False)] #[(True, True), (False, False)] #(FxSh, Independant)
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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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for n_tf in N_seq_TF:
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for dist in dist_mix:
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for m_setup in mag_setup:
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torch.cuda.reset_max_memory_allocated() #reset_peak_stats
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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, num_classes=len(dl_train.dataset.classes))
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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=False,
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fixed_mag=m_setup[0],
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shared_mag=m_setup[1],
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TF_ignore_mag=tf_ignore_mag),
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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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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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exec_time=time.perf_counter() - t0
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max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
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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_allocated, max_cached],
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"TF_config": tf_config,
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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/Vanilla ###
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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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for run in range(nb_run):
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torch.cuda.reset_max_memory_allocated() #reset_peak_stats
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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, num_classes=len(dl_train.dataset.classes)).to(device)
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print("{} on {} for {} epochs".format(model_name, device_name, epochs))
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#print("RandAugment(N{}-M{:.2f})-{} 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_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
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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_allocated, max_cached],
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#"Rand_Aug": rand_aug,
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"Log": log}
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print(model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{} epochs -{}".format(model_name,epochs, run)
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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{:.2f})-{}-{} 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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print(sys.exc_info()[1])
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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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'''
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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 = [1.0]#[0.0, 0.5, 0.8, 1.0]
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N_seq_TF= [5, 6]
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mag_setup = [(True, True), (False, False)] #(FxSh, Independant)
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#prob_setup = [True, False]
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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 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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t0 = time.perf_counter()
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model = getattr(models.resnet, 'resnet18')(pretrained=False, num_classes=len(dl_train.dataset.classes))
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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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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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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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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_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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