<jupyter_start><jupyter_text>----------------------------------------------------------------------<jupyter_code>DO='TRAIN'
random.seed(47)
combo_fn = SUM
lambda_reg = 1
def test(net,classifier, loader):

      net.to(dev)
      classifier.to(dev)

      net.eval()

      sum_accuracy = 0

      # Process each batch
      for j, (input, labels) in enumerate(loader):

        input = input.to(dev)
        labels = labels.float().to(dev)

        features = net(input)

        pred = torch.squeeze(classifier(features))

        # https://discuss.pytorch.org/t/bcewithlogitsloss-and-model-accuracy-calculation/59293/ 2
        #pred_labels = (pred >= 0.0).long()  # Binarize predictions to 0 and 1
        _,pred_label = torch.max(pred, dim = 1)
        pred_labels = (pred_label == labels).float()

        batch_accuracy = pred_labels.sum().item() / len(labels)

        # Update accuracy
        sum_accuracy += batch_accuracy

      epoch_accuracy = sum_accuracy / len(loader)
      return epoch_accuracy
      #print(f"Accuracy test: {epoch_accuracy:0.5}")
def train(nets, loaders, optimizer, criterion, epochs=20, dev=None, save_param=False, model_name="valerio"):
    # try:
      nets = [n.to(dev) for n in nets]

      model_a = module_unwrap(nets[0], True)
      model_b = module_unwrap(nets[1], True)
      model_c = module_unwrap(nets[2], True)

      reg_loss = nn.MSELoss()

      criterion.to(dev)
      reg_loss.to(dev)

      # Initialize history
      history_loss = {"train": [], "val": [], "test": []}
      history_accuracy = {"train": [], "val": [], "test": []}
      history_test = []
      # Store the best val accuracy
      best_val_accuracy = 0

      # Process each epoch
      for epoch in range(epochs):
        # Initialize epoch variables
        sum_loss = {"train": 0, "val": 0, "test": 0}
        sum_accuracy = {"train": [0,0,0], "val": [0,0,0], "test": [0,0,0]}

        progbar = None
        # Process each split
        for split in ["train", "val", "test"]:
          if split == "train":
            for n in nets:
              n.train()
            widgets = [
              ' [', pb.Timer(), '] ',
              pb.Bar(),
              ' [', pb.ETA(), '] ', pb.Variable('ta','[Train Acc: {formatted_value}]')
            ]

            progbar = pb.ProgressBar(max_value=len(loaders[split][0]),widgets=widgets,redirect_stdout=True)

          else:
            for n in nets:
              n.eval()
          # Process each batch
          for j,((input_a, labels_a),(input_b, labels_b)) in enumerate(zip(loaders[split][0],loaders[split][1])):

            input_a = input_a.to(dev)
            input_b = input_b.to(dev)

            labels_a = labels_a.long().to(dev)
            labels_b = labels_b.long().to(dev)
            #print(labels_a.shape)
            #labels_a = labels_a.squeeze()
            #labels_b = labels_b.squeeze()
            
            #labels_a = labels_a.unsqueeze(1)
            #labels_b = labels_b.unsqueeze(1)
            #print(labels_a.shape)
            #labels_a = labels_a.argmax(-1)
            #labels_b = labels_b.argmax(-1)

            inputs = torch.cat([input_a,input_b],axis=0)
            labels = torch.cat([labels_a, labels_b])

            #labels  = labels.squeeze()
            #print(labels.shape)
            #labels = labels.argmax(-1)

            # Reset gradients
            optimizer.zero_grad()
            # Compute output
            features_a = nets[0](input_a)
            features_b = nets[1](input_b)
            features_c = nets[2](inputs)

            pred_a = torch.squeeze(nets[3](features_a))
            pred_b = torch.squeeze(nets[3](features_b))
            pred_c = torch.squeeze(nets[3](features_c))

            loss = criterion(pred_a, labels_a) + criterion(pred_b, labels_b) + criterion(pred_c, labels)

            for n in model_a:
              layer_a = model_a[n]
              layer_b = model_b[n]
              layer_c = model_c[n]
              if (isinstance(layer_a,nn.Conv2d)):
                loss += lambda_reg * reg_loss(combo_fn(layer_a.weight,layer_b.weight),layer_c.weight)
                if (layer_a.bias is not None):
                  loss += lambda_reg * reg_loss(combo_fn(layer_a.bias, layer_b.bias), layer_c.bias)

            # Update loss
            sum_loss[split] += loss.item()
            # Check parameter update
            if split == "train":
              # Compute gradients
              loss.backward()
              # Optimize
              optimizer.step()

            # Compute accuracy

            #https://discuss.pytorch.org/t/bcewithlogitsloss-and-model-accuracy-calculation/59293/ 2
            #pred_labels_a = (pred_a >= 0.0).long()  # Binarize predictions to 0 and 1
            #pred_labels_b = (pred_b >= 0.0).long()  # Binarize predictions to 0 and 1
            #pred_labels_c = (pred_c >= 0.0).long()  # Binarize predictions to 0 and 1

            #print(pred_a.shape)

            _,pred_label_a = torch.max(pred_a, dim = 1)
            pred_labels_a = (pred_label_a == labels_a).float()

            _,pred_label_b = torch.max(pred_b, dim = 1)
            pred_labels_b = (pred_label_b == labels_b).float()

            _,pred_label_c = torch.max(pred_c, dim = 1)
            pred_labels_c = (pred_label_c == labels).float()

            batch_accuracy_a = pred_labels_a.sum().item() / len(labels_a)
            batch_accuracy_b = pred_labels_b.sum().item() / len(labels_b)
            batch_accuracy_c = pred_labels_c.sum().item() / len(labels)

            # Update accuracy
            sum_accuracy[split][0] += batch_accuracy_a
            sum_accuracy[split][1] += batch_accuracy_b
            sum_accuracy[split][2] += batch_accuracy_c


            if (split=='train'):
              progbar.update(j, ta=batch_accuracy_c)

        if (progbar is not None):
          progbar.finish()
        # Compute epoch loss/accuracy
        epoch_loss = {split: sum_loss[split] / len(loaders[split][0]) for split in ["train", "val", "test"]}
        epoch_accuracy = {split: [sum_accuracy[split][i] / len(loaders[split][0]) for i in range(len(sum_accuracy[split])) ] for split in ["train", "val", "test"]}

        # # Store params at the best validation accuracy
        # if save_param and epoch_accuracy["val"] > best_val_accuracy:
        #   # torch.save(net.state_dict(), f"{net.__class__.__name__}_best_val.pth")
        #   torch.save(net.state_dict(), f"{model_name}_best_val.pth")
        #   best_val_accuracy = epoch_accuracy["val"]

        print(f"Epoch {epoch + 1}:")
        # Update history
        for split in ["train", "val", "test"]:
          history_loss[split].append(epoch_loss[split])
          history_accuracy[split].append(epoch_accuracy[split])
          # Print info
          print(f"\t{split}\tLoss: {epoch_loss[split]:0.5}\tVGG 1:{epoch_accuracy[split][0]:0.5}"
                f"\tVGG 2:{epoch_accuracy[split][1]:0.5}\tVGG *:{epoch_accuracy[split][2]:0.5}")

        if save_param:
          torch.save({'vgg_a':nets[0].state_dict(),'vgg_b':nets[1].state_dict(),'vgg_star':nets[2].state_dict(),'classifier':nets[3].state_dict()},f'{model_name}.pth')


        
        print(f"Accuracy test VGGA: {test(nets[0], nets[3], test_loader_all):0.5}")
        print(f"Accuracy test VGGB: {test(nets[1], nets[3], test_loader_all):0.5}")
        print(f"Accuracy test VGG*: {test(nets[2], nets[3], test_loader_all):0.5}")
 
        summed_state_dict = OrderedDict()
 
        for key in nets[2].state_dict():
          if key.find('conv') >=0:
            #print(key)
            summed_state_dict[key] = combo_fn(nets[0].state_dict()[key],nets[1].state_dict()[key])
          else:
            summed_state_dict[key] = nets[2].state_dict()[key]
 
        nets[2].load_state_dict(summed_state_dict)
        accuracy_star = test(nets[2], nets[3], test_loader_all)
        print(f"Accuracy test VGGSTAR: {accuracy_star:0.5}")
        history_test.append(accuracy_star)


      # Plot accuracy
      plt.title("Accuracy VGGSTAR over epochs")
      plt.plot(history_test)
      #plt.legend()
      plt.show()

root_dir = './'
rescale_data = transforms.Lambda(lambda x : x/255)

# Compose transformations
data_transform = transforms.Compose([
  transforms.Resize(32),
  transforms.RandomHorizontalFlip(),
  transforms.ToTensor(),
  rescale_data,
  #transforms.Normalize((-0.7376), (0.5795))
])

test_transform = transforms.Compose([
  transforms.Resize(32),
  transforms.ToTensor(),
  rescale_data,
  #transforms.Normalize((0.1327), (0.2919))
])
# Load MNIST dataset with transforms
train_set = torchvision.datasets.MNIST(root=root_dir, train=True, download=True, transform=data_transform)
test_set = torchvision.datasets.MNIST(root=root_dir, train=False, download=True, transform=test_transform)
train_idx = np.random.permutation(np.arange(len(train_set)))
test_idx = np.arange(len(test_set))

val_frac = 0.1

n_val = int(len(train_idx) * val_frac)
val_idx = train_idx[0:n_val]
train_idx = train_idx[n_val:]

h = len(train_idx)//2

train_set_a = Subset(train_set,train_idx[0:h])
train_set_b = Subset(train_set,train_idx[h:])

h = len(val_idx)//2

val_set_a = Subset(train_set,val_idx[0:h])
val_set_b = Subset(train_set,val_idx[h:])

h = len(test_idx)//2

test_set_a = Subset(test_set,test_idx[0:h])
test_set_b = Subset(test_set,test_idx[h:])
# Define loaders

train_loader_a = DataLoader(train_set_a, batch_size=64, num_workers=0, shuffle=True, drop_last=True)
val_loader_a   = DataLoader(val_set_a,   batch_size=64, num_workers=0, shuffle=False, drop_last=False)
test_loader_a  = DataLoader(test_set_a,  batch_size=64, num_workers=0, shuffle=False, drop_last=False)

train_loader_b = DataLoader(train_set_b, batch_size=64, num_workers=0, shuffle=True, drop_last=True)
val_loader_b   = DataLoader(val_set_b,   batch_size=64, num_workers=0, shuffle=False, drop_last=False)
test_loader_b  = DataLoader(test_set_b,  batch_size=64, num_workers=0, shuffle=False, drop_last=False)

test_loader_all = DataLoader(test_set,batch_size=64, num_workers=0,shuffle=False,drop_last=False)


# Define dictionary of loaders
loaders = {"train": [train_loader_a,train_loader_b],
           "val":   [val_loader_a,val_loader_b],
           "test":  [test_loader_a,test_loader_b]}
class VGG16INIT(nn.Module):

  def __init__(self, input_size, model1, model2, batch_norm=False):
    super(VGG16INIT, self).__init__()

    self.in_channels,self.in_width,self.in_height = input_size

    self.block_1 = VGGBlock(self.in_channels,64,batch_norm=batch_norm)
    self.block_2 = VGGBlock(64, 128,batch_norm=batch_norm)
    self.block_3 = VGGBlock(128, 256,batch_norm=batch_norm)
    self.block_4 = VGGBlock(256,512,batch_norm=batch_norm)

    for p_out, p_in1, p_in2 in zip(self.parameters(), model1.parameters(), model2.parameters()):
            p_out.data = nn.Parameter(p_in1 +p_in2);

  @property
  def input_size(self):
      return self.in_channels,self.in_width,self.in_height


  def forward(self, x):

    x = self.block_1(x)
    x = self.block_2(x)
    x = self.block_3(x)
    x = self.block_4(x)
    # x = self.avgpool(x)
    x = torch.flatten(x,1)

    return x
model1 = VGG16((1,32,32),batch_norm=True)
model2 = VGG16((1,32,32),batch_norm=True)
model3 = VGG16((1,32,32),batch_norm=True)
#model3 = VGG16INIT((1,32,32),model1,model2,batch_norm=True)
classifier = Classifier(num_classes=10)
#list(model1.parameters())
#list(model2.parameters())
#list(model3.parameters())
nets = [model1,model2,model3,classifier]
dev = torch.device('cuda')
parameters = set()
for n in nets:
  parameters |= set(n.parameters())
optimizer = torch.optim.SGD(parameters, lr = 0.01)
# Define a loss
#criterion = nn.BCEWithLogitsLoss()#,nn.BCEWithLogitsLoss(),nn.BCEWithLogitsLoss(),_lambda = 1)
criterion = nn.CrossEntropyLoss()
n_params = 0
DO = 'TRAIN'
if (DO=='TRAIN'):
  train(nets, loaders, optimizer, criterion, epochs=100, dev=dev,save_param=True)
else:
  state_dicts = torch.load('model.pth')
  model1.load_state_dict(state_dicts['vgg_a']) #questi state_dict vengono dalla funzione di training
  model2.load_state_dict(state_dicts['vgg_b'])
  model3.load_state_dict(state_dicts['vgg_star'])
  classifier.load_state_dict(state_dicts['classifier'])

  test(model1,classifier,test_loader_all)
  test(model2, classifier, test_loader_all)
  test(model3, classifier, test_loader_all)

  summed_state_dict = OrderedDict()

  for key in state_dicts['vgg_star']:
    if key.find('conv') >=0:
      print(key)
      summed_state_dict[key] = combo_fn(state_dicts['vgg_a'][key],state_dicts['vgg_b'][key])
    else:
      summed_state_dict[key] = state_dicts['vgg_star'][key]

  model3.load_state_dict(summed_state_dict)
  test(model3, classifier, test_loader_all)
!pip install --upgrade progressbar2<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '166338', 'license': 'MIT', 'n_tokens_mistral': 4732, 'n_tokens_neox': 4498, 'n_words': 743, 'path': '3_VALERIO_PT3_SHARED_CLASSIFIER_MULTICLASS.ipynb', 'repo_name': 'CasellaJr/Federated-Transfer-Learning-using-Network-Composition', 'size': '12793'}