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Log et utils pour Dataugv5
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Harle, Antoine (Contracteur)
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6 changed files with 57 additions and 45 deletions
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@ -2,21 +2,18 @@ from utils import *
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if __name__ == "__main__":
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#### Comparison ####
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## Loss , Acc, Proba = f(epoch) ##
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'''
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files=[
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#"res/log/LeNet-100 epochs.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-4 TF)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-4 TF)-LeNet)-100 epochs (dataug:50)- 0 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-3 TF)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/log/Aug_mod(Data_augV3(Uniform-3 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Mix 0,5-3 TF)-LeNet)-100 epochs (dataug:0)- 1 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Mix 0.5-3 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-3 TF)-LeNet)-100 epochs (dataug:0)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-10 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
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#"res/log/Aug_mod(Data_augV4(Uniform-10 TF)-LeNet)-100 epochs (dataug:50)- 0 in_it.json",
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"res/log/Aug_mod(Data_augV5(Mix0.5-11TFx1-MagSh)-LeNet)-2 epochs (dataug:0)- 1 in_it.json",
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]
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for idx, file in enumerate(files):
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#legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
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'''
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## Loss , Acc, Proba = f(epoch) ##
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#plot_compare(filenames=files, fig_name="res/compare")
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## Acc, Time, Epochs = f(n_tf) ##
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@ -38,8 +38,8 @@ data_test = torchvision.datasets.CIFAR10(
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"./data", train=False, download=True, transform=transform
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)
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#'''
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train_subset_indices=range(int(len(data_train)/2))
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#train_subset_indices=range(BATCH_SIZE*10)
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#train_subset_indices=range(int(len(data_train)/2))
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train_subset_indices=range(BATCH_SIZE*10)
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val_subset_indices=range(int(len(data_train)/2),len(data_train))
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dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
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@ -531,7 +531,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
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return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF)
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class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, shared_mag=True):
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_mag=True, shared_mag=True):
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super(Data_augV5, self).__init__()
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assert len(TF_dict)>0
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@ -543,14 +543,14 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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self._N_seqTF = N_TF
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self._shared_mag = shared_mag
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self._fixed_mag = fixed_mag
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#self._fixed_mag=5 #[0, PARAMETER_MAX]
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self._params = nn.ParameterDict({
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"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
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"mag" : nn.Parameter(torch.tensor(0.5) if shared_mag
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"mag" : nn.Parameter(torch.tensor(0.5) if self._shared_mag
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else torch.tensor(0.5).expand(self._nb_tf)), #[0, PARAMETER_MAX]/10
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})
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self._samples = []
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self._mix_dist = False
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@ -594,23 +594,19 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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if smp_x.shape[0]!=0: #if there's data to TF
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magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx]
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if self._fixed_mag: magnitude=magnitude.detach() #Fmodel tente systematiquement de tracker les gradient de tout les param
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tf=self._TF[tf_idx]
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#print(magnitude)
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#x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) # Refusionner eviter x[mask] : in place
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#In place
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#x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude)
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#Out of place
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smp_x = self._TF_dict[tf](x=smp_x, mag=magnitude)
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idx= mask.nonzero()
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#print('-'*8)
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idx= idx.expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
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#print(idx.shape, smp_x.shape)
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#print(idx[0], tf_idx)
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#print(smp_x[0,])
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#x=x.view(-1,3*32*32)
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#smp_x=smp_x.view(-1,3*32*32)
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x=x.scatter(dim=0, index=idx, src=smp_x)
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#x=x.view(-1,3,32,32)
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#print(x[0,])
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return x
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@ -663,10 +659,13 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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return self._params[key]
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def __str__(self):
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mag_param='Mag'
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if self._fixed_mag: mag_param+= 'Fx'
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if self._shared_mag: mag_param+= 'Sh'
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if not self._mix_dist:
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return "Data_augV5(Uniform-%d TF x %d)" % (self._nb_tf, self._N_seqTF)
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return "Data_augV5(Uniform-%dTFx%d-%s)" % (self._nb_tf, self._N_seqTF, mag_param)
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else:
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return "Data_augV5(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF)
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return "Data_augV5(Mix%.1f-%dTFx%d-%s)" % (self._mix_factor, self._nb_tf, self._N_seqTF, mag_param)
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class Augmented_model(nn.Module):
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@ -68,7 +68,7 @@ if __name__ == "__main__":
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t0 = time.process_time()
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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#tf_dict = TF.TF_dict
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.5, shared_mag=True), LeNet(3,10)).to(device)
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.5, fixed_mag=False, shared_mag=True), LeNet(3,10)).to(device)
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#aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).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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@ -650,9 +650,11 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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print('TF Proba :', model['data_aug']['prob'].data)
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#print('proba grad',model['data_aug']['prob'].grad)
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print('TF Mag :', model['data_aug']['mag'].data)
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print('Mag grad',model['data_aug']['mag'].grad)
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#print('Mag grad',model['data_aug']['mag'].grad)
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#############
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#### Log ####
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#print(type(model['data_aug']) is dataug.Data_augV5)
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param = [{'p': p.item(), 'm':model['data_aug']['mag'].item()} for p in model['data_aug']['prob']] if model['data_aug']._shared_mag else [{'p': p.item(), 'm': m.item()} for p, m in zip(model['data_aug']['prob'], model['data_aug']['mag'])]
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data={
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"epoch": epoch,
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"train_loss": loss.item(),
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@ -660,7 +662,8 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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"acc": accuracy,
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"time": tf - t0,
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"param": [p.item() for p in model['data_aug']['prob']],
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"param": param #if isinstance(model['data_aug'], Data_augV5)
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#else [p.item() for p in model['data_aug']['prob']],
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}
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log.append(data)
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#############
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@ -52,28 +52,41 @@ def plot_resV2(log, fig_name='res', param_names=None):
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epochs = [x["epoch"] for x in log]
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fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(15, 15))
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fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(30, 15))
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ax[0, 0].set_title('Loss')
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ax[0, 0].plot(epochs,[x["train_loss"] for x in log], label='Train')
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ax[0, 0].plot(epochs,[x["val_loss"] for x in log], label='Val')
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ax[0, 0].legend()
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ax[0, 1].set_title('Acc')
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ax[0, 1].plot(epochs,[x["acc"] for x in log])
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ax[1, 0].set_title('Acc')
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ax[1, 0].plot(epochs,[x["acc"] for x in log])
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if log[0]["param"]!= None:
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ax[1, 1].set_title('Prob')
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if not param_names : param_names = ['P'+str(idx) for idx, _ in enumerate(log[0]["param"])]
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proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])]
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ax[1, 1].stackplot(epochs, proba, labels=param_names)
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ax[1, 1].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
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#proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])]
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proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])]
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mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])]
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ax[1, 0].set_title('Mean prob')
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mean = np.mean([x["param"] for x in log], axis=0)
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std = np.std([x["param"] for x in log], axis=0)
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ax[1, 0].bar(param_names, mean, yerr=std)
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plt.sca(ax[1, 0]), plt.xticks(rotation=90)
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ax[0, 1].set_title('Prob =f(epoch)')
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ax[0, 1].stackplot(epochs, proba, labels=param_names)
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#ax[0, 1].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
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ax[1, 1].set_title('Prob =f(TF)')
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mean = np.mean(proba, axis=1)
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std = np.std(proba, axis=1)
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ax[1, 1].bar(param_names, mean, yerr=std)
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plt.sca(ax[1, 1]), plt.xticks(rotation=90)
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ax[0, 2].set_title('Mag =f(epoch)')
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ax[0, 2].stackplot(epochs, mag, labels=param_names)
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ax[0, 2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
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ax[1, 2].set_title('Mag =f(TF)')
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mean = np.mean(mag, axis=1)
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std = np.std(mag, axis=1)
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ax[1, 2].bar(param_names, mean, yerr=std)
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plt.sca(ax[1, 2]), plt.xticks(rotation=90)
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fig_name = fig_name.replace('.',',')
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