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https://github.com/AntoineHX/smart_augmentation.git
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Correction test MobileNet Brutus
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Harle, Antoine (Contracteur)
parent
48c3925d74
commit
6c0597e7ea
4 changed files with 140 additions and 22 deletions
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@ -82,7 +82,7 @@ if __name__ == "__main__":
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nb_run=3
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accs = []
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times = []
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files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-150epochs(dataug:0)-0in_it-%s.json"%str(run) for run in range(nb_run)]
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files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-Mag)-LeNet)-150epochs(dataug:0)-1in_it-%s.json"%str(run) for run in range(nb_run)]
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for idx, file in enumerate(files):
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#legend+=str(idx)+'-'+file+'\n'
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@ -50,7 +50,6 @@ class AugmentedDataset(VisionDataset):
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for idx, img in enumerate(self.sup_data):
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self.sup_data[idx]= Image.fromarray(img) #to PIL Image
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self.unsup_ratio=5 #Batch size unsup = train batch size * unsup_ratio
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self.unsup_data=[]
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self.unsup_targets=[]
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@ -157,6 +156,120 @@ class AugmentedDataset(VisionDataset):
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def __str__(self):
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return "CIFAR10(Sup:{}-Unsup:{}-{}TF)".format(self.dataset_info['sup'], self.dataset_info['unsup'], len(self._TF))
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class AugmentedDatasetV2(VisionDataset):
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def __init__(self, root, train=True, transform=None, target_transform=None, download=False, subset=None):
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super(AugmentedDatasetV2, self).__init__(root, transform=transform, target_transform=target_transform)
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supervised_dataset = torchvision.datasets.CIFAR10(root, train=train, download=download, transform=transform)
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self.sup_data = supervised_dataset.data if not subset else supervised_dataset.data[subset[0]:subset[1]]
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self.sup_targets = supervised_dataset.targets if not subset else supervised_dataset.targets[subset[0]:subset[1]]
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assert len(self.sup_data)==len(self.sup_targets)
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for idx, img in enumerate(self.sup_data):
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self.sup_data[idx]= Image.fromarray(img) #to PIL Image
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self.unsup_data=[]
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self.unsup_targets=[]
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self.origin_idx=[]
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self.dataset_info= {
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'name': 'CIFAR10',
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'sup': len(self.sup_data),
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'unsup': len(self.unsup_data),
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'length': len(self.sup_data)+len(self.unsup_data),
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}
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self._TF = [
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## Geometric TF ##
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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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'Cutout',
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## Color TF ##
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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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'Invert',
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'AutoContrast',
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'Equalize',
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]
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self._op_list =[]
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self.prob=0.5
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for tf in self._TF:
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for mag in range(1, 10):
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self._op_list+=[(tf, self.prob, mag)]
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self._nb_op = len(self._op_list)
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def __getitem__(self, index):
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where target is index of the target class.
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"""
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aug_img, origin_img, target = self.unsup_data[index], self.sup_data[self.origin_idx[index]], self.unsup_targets[index]
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# doing this so that it is consistent with all other datasets
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# to return a PIL Image
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#img = Image.fromarray(img)
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if self.transform is not None:
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aug_img = self.transform(aug_img)
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origin_img = self.transform(origin_img)
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if self.target_transform is not None:
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target = self.target_transform(target)
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return aug_img, origin_img, target
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def augement_data(self, aug_copy=1):
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policies = []
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for op_1 in self._op_list:
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for op_2 in self._op_list:
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policies += [[op_1, op_2]]
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for idx, image in enumerate(self.sup_data):
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if idx%(self.dataset_info['sup']/5)==0: print("Augmenting data... ", idx,"/", self.dataset_info['sup'])
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#if idx==10000:break
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for _ in range(aug_copy):
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chosen_policy = policies[np.random.choice(len(policies))]
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aug_image = augmentation_transforms.apply_policy(chosen_policy, image, use_mean_std=False) #Cast en float image
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#aug_image = augmentation_transforms.cutout_numpy(aug_image)
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self.unsup_data+=[(aug_image*255.).astype(self.sup_data.dtype)]#Cast float image to uint8
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self.unsup_targets+=[self.sup_targets[idx]]
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self.origin_idx+=[idx]
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#self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
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self.unsup_data=np.array(self.unsup_data)
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assert len(self.unsup_data)==len(self.unsup_targets)
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self.dataset_info['unsup']=len(self.unsup_data)
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self.dataset_info['length']=self.dataset_info['sup']+self.dataset_info['unsup']
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def __len__(self):
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return self.dataset_info['unsup']#self.dataset_info['length']
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def __str__(self):
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return "CIFAR10(Sup:{}-Unsup:{}-{}TF)".format(self.dataset_info['sup'], self.dataset_info['unsup'], len(self._TF))
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### Classic Dataset ###
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data_train = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
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#data_val = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
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@ -35,7 +35,7 @@ if __name__ == "__main__":
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n_inner_iter = 1
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epochs = 150
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epochs = 200
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dataug_epoch_start=0
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#model = LeNet(3,10)
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@ -44,22 +44,6 @@ if __name__ == "__main__":
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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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=True, fixed_mag=True, shared_mag=True), 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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t0 = time.process_time()
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@ -77,6 +61,23 @@ 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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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-tests/"
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epochs= 150
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@ -629,7 +629,7 @@ def run_dist_dataug(model, epochs=1, inner_it=1, dataug_epoch_start=0):
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print("Copy ", countcopy)
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return log
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def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_freq=1, KLdiv=False, loss_patience=None):
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def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_freq=1, KLdiv=False, loss_patience=None, save_sample=False):
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device = next(model.parameters()).device
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log = []
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countcopy=0
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@ -796,8 +796,12 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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model.augment(mode=True)
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if inner_it != 0: high_grad_track = True
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viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
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viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
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try:
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viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
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viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
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except:
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print("Couldn't save finals samples")
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pass
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#print("Copy ", countcopy)
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return log
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