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Modif interface Data_augv4
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Harle, Antoine (Contracteur)
parent
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commit
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2 changed files with 77 additions and 98 deletions
105
higher/dataug.py
105
higher/dataug.py
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@ -320,31 +320,6 @@ class Data_augV4(nn.Module): #Transformations avec mask
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#self._TF_matrix={}
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#self._input_info={'h':0, 'w':0, 'device':None} #Input associe a TF_matrix
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'''
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self._mag_fct={ #f(mag_normalise)=mag_reelle
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## Geometric TF ##
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'Identity' : (lambda mag: None),
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'FlipUD' : (lambda mag: None),
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'FlipLR' : (lambda mag: None),
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'Rotate': (lambda mag: random.randint(-int_parameter(mag, maxval=30), int_parameter(mag, maxval=30))),
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'TranslateX': (lambda mag: [random.randint(-int_parameter(mag, maxval=20), int_parameter(mag, maxval=20)), 0]),
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'TranslateY': (lambda mag: [0, random.randint(-int_parameter(mag, maxval=20), int_parameter(mag, maxval=20))]),
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'ShearX': (lambda mag: [random.uniform(-float_parameter(mag, maxval=0.3), float_parameter(mag, maxval=0.3)), 0]),
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'ShearY': (lambda mag: [0, random.uniform(-float_parameter(mag, maxval=0.3), float_parameter(mag, maxval=0.3))]),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
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'Color':(lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
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'Brightness':(lambda mag: random.uniform(1., float_parameter(mag, maxval=1.9))),
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'Sharpness':(lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
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'Posterize': (lambda mag: random.randint(4, int_parameter(mag, maxval=8))),
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'Solarize': (lambda mag: random.randint(1, int_parameter(mag, maxval=256))/256.), #=>Image entre [0,1] #Pas opti pour des batch
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#Non fonctionnel
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'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
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#'Equalize': (lambda mag: None),
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}
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'''
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self._mag_fct = TF_dict
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self._TF=list(self._mag_fct.keys())
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self._nb_tf= len(self._TF)
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@ -380,12 +355,52 @@ class Data_augV4(nn.Module): #Transformations avec mask
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self._sample = cat_distrib.sample()
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## Transformations ##
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#'''
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x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
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x = self.apply_TF(x, self._sample)
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return x
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'''
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def compute_TF_matrix(self, magnitude=None, sample_info= None):
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print('Computing TF_matrix...')
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if not magnitude :
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magnitude=self._fixed_mag
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if sample_info:
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self._input_info['h']= sample_info['h']
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self._input_info['w']= sample_info['w']
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self._input_info['device'] = sample_info['device']
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h, w, device= self._input_info['h'], self._input_info['w'], self._input_info['device']
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self._TF_matrix={}
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for tf in self._TF :
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if tf=='Id':
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self._TF_matrix[tf]=torch.tensor([[[ 1., 0., 0.],
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[ 0., 1., 0.],
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[ 0., 0., 1.]]], device=device)
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elif tf=='Rot':
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center = torch.ones(1, 2, device=device)
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center[0, 0] = w / 2 # x
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center[0, 1] = h / 2 # y
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scale = torch.ones(1, device=device)
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angle = self._mag_fct[tf](magnitude) * torch.ones(1, device=device)
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R = kornia.get_rotation_matrix2d(center, angle, scale) #Rotation matrix (1,2,3)
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self._TF_matrix[tf]=torch.cat((R,torch.tensor([[[ 0., 0., 1.]]], device=device)), dim=1) #TF matrix (1,3,3)
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elif tf=='FlipLR':
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self._TF_matrix[tf]=torch.tensor([[[-1., 0., w-1],
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[ 0., 1., 0.],
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[ 0., 0., 1.]]], device=device)
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elif tf=='FlipUD':
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self._TF_matrix[tf]=torch.tensor([[[ 1., 0., 0.],
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[ 0., -1., h-1],
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[ 0., 0., 1.]]], device=device)
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else:
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raise Exception("Invalid TF requested")
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'''
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def apply_TF(self, x, sampled_TF):
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device = x.device
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smps_x=[]
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masks=[]
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for tf_idx in range(self._nb_tf):
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mask = self._sample==tf_idx #Create selection mask
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mask = sampled_TF==tf_idx #Create selection mask
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smp_x = x[mask] #torch.masked_select() ?
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if smp_x.shape[0]!=0: #if there's data to TF
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@ -452,43 +467,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
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x=kornia.warp_perspective(x, TF_matrix, dsize=(h, w))
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'''
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return x
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'''
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def compute_TF_matrix(self, magnitude=None, sample_info= None):
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print('Computing TF_matrix...')
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if not magnitude :
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magnitude=self._fixed_mag
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if sample_info:
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self._input_info['h']= sample_info['h']
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self._input_info['w']= sample_info['w']
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self._input_info['device'] = sample_info['device']
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h, w, device= self._input_info['h'], self._input_info['w'], self._input_info['device']
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self._TF_matrix={}
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for tf in self._TF :
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if tf=='Id':
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self._TF_matrix[tf]=torch.tensor([[[ 1., 0., 0.],
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[ 0., 1., 0.],
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[ 0., 0., 1.]]], device=device)
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elif tf=='Rot':
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center = torch.ones(1, 2, device=device)
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center[0, 0] = w / 2 # x
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center[0, 1] = h / 2 # y
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scale = torch.ones(1, device=device)
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angle = self._mag_fct[tf](magnitude) * torch.ones(1, device=device)
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R = kornia.get_rotation_matrix2d(center, angle, scale) #Rotation matrix (1,2,3)
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self._TF_matrix[tf]=torch.cat((R,torch.tensor([[[ 0., 0., 1.]]], device=device)), dim=1) #TF matrix (1,3,3)
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elif tf=='FlipLR':
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self._TF_matrix[tf]=torch.tensor([[[-1., 0., w-1],
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[ 0., 1., 0.],
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[ 0., 0., 1.]]], device=device)
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elif tf=='FlipUD':
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self._TF_matrix[tf]=torch.tensor([[[ 1., 0., 0.],
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[ 0., -1., h-1],
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[ 0., 0., 1.]]], device=device)
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else:
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raise Exception("Invalid TF requested")
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'''
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def adjust_prob(self, soft=False): #Detach from gradient ?
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if soft :
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@ -646,8 +646,8 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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tf = time.process_time()
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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=aug_model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
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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=aug_model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
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if(not high_grad_track):
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countcopy+=1
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@ -732,7 +732,7 @@ if __name__ == "__main__":
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aug_model = Augmented_model(Data_augV4(TF_dict=TF.TF_dict, mix_dist=0.0), LeNet(3,10)).to(device)
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print(str(aug_model), 'on', device_name)
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#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
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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, loss_patience=10)
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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=1, loss_patience=10)
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####
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plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
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