123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129 |
- import copy
- import time
- import torch
- import torch.optim as optim
- from torch import nn
- # TODO: get these properly
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- feature_extract = True
- model_ft = None # TODO
- dataloaders_dict = None # TODO
- model_name = None # TODO
- def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False):
- since = time.time()
- val_acc_history = []
- best_model_wts = copy.deepcopy(model.state_dict())
- best_acc = 0.0
- # Detect if we have a GPU available
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- for epoch in range(num_epochs):
- print('Epoch {}/{}'.format(epoch, num_epochs - 1))
- print('-' * 10)
- # Each epoch has a training and validation phase
- for phase in ['train', 'val']:
- if phase == 'train':
- model.train() # Set model to training mode
- else:
- model.eval() # Set model to evaluate mode
- running_loss = 0.0
- running_corrects = 0
- # Iterate over data.
- for inputs, labels in dataloaders[phase]:
- inputs = inputs.to(device)
- labels = labels.to(device)
- # zero the parameter gradients
- optimizer.zero_grad()
- # forward
- # track history if only in train
- with torch.set_grad_enabled(phase == 'train'):
- # Get model outputs and calculate loss
- # Special case for inception because in training it has an auxiliary output. In train
- # mode we calculate the loss by summing the final output and the auxiliary output
- # but in testing we only consider the final output.
- if is_inception and phase == 'train':
- # https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
- outputs, aux_outputs = model(inputs)
- loss1 = criterion(outputs, labels)
- loss2 = criterion(aux_outputs, labels)
- loss = loss1 + 0.4 * loss2
- else:
- outputs = model(inputs)
- loss = criterion(outputs, labels)
- _, preds = torch.max(outputs, 1)
- # backward + optimize only if in training phase
- if phase == 'train':
- loss.backward()
- optimizer.step()
- # statistics
- running_loss += loss.item() * inputs.size(0)
- running_corrects += torch.sum(preds == labels.data)
- epoch_loss = running_loss / len(dataloaders[phase].dataset)
- epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
- print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
- # deep copy the model
- if phase == 'val' and epoch_acc > best_acc:
- best_acc = epoch_acc
- best_model_wts = copy.deepcopy(model.state_dict())
- if phase == 'val':
- val_acc_history.append(epoch_acc)
- print()
- time_elapsed = time.time() - since
- print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
- print('Best val Acc: {:4f}'.format(best_acc))
- # load best model weights
- model.load_state_dict(best_model_wts)
- return model, val_acc_history
- # Send the model to GPU
- model_ft = model_ft.to(device)
- # Gather the parameters to be optimized/updated in this run. If we are
- # finetuning we will be updating all parameters. However, if we are
- # doing feature extract method, we will only update the parameters
- # that we have just initialized, i.e. the parameters with requires_grad
- # is True.
- params_to_update = model_ft.parameters()
- print("Params to learn:")
- if feature_extract:
- params_to_update = []
- for name, param in model_ft.named_parameters():
- if param.requires_grad is True:
- params_to_update.append(param)
- print("\t", name)
- else:
- for name, param in model_ft.named_parameters():
- if param.requires_grad is True:
- print("\t", name)
- # Observe that all parameters are being optimized
- optimizer_ft = optim.SGD(params_to_update, lr=0.001, momentum=0.9)
- # Setup the loss fxn
- criterion = nn.CrossEntropyLoss()
- # Train and evaluate
- model_ft, hist = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, num_epochs=num_epochs,
- is_inception=(model_name == "inception"))
|