<filename>utils/train_model.py<gh_stars>1-10
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

def train_model(model, criterion, optimizer, scheduler, dataloaders, dataset_sizes, device, num_epochs=25):
    since = time.time()
    training_log = {'epoch':[], 'training_loss':[], 'training_acc':[], 'val_loss':[], 'val_acc':[], 'best_acc':0.0}

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        training_log['epoch'].append(epoch)
        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'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # 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)
            if phase == 'train':
                if scheduler != None:
                    scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            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 == 'train':
                training_log['training_loss'].append(epoch_loss)
                training_log['training_acc'].append(epoch_acc.to('cpu').numpy())
            if phase == 'val':
                training_log['val_loss'].append(epoch_loss)
                training_log['val_acc'].append(epoch_acc.to('cpu').numpy())

        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))
    training_log['best_acc'] = best_acc.to('cpu').numpy()
    training_log['training_loss'] = np.array(training_log['training_loss'])
    training_log['training_acc'] = np.array(training_log['training_acc'])
    training_log['val_loss'] = np.array(training_log['val_loss'])
    training_log['val_acc'] = np.array(training_log['val_acc'])

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model, training_log

def trans_to_cuda(variable):
    if torch.cuda.is_available():
        return variable.cuda()
    else:
        return variable
<filename>utils/train_model.py<gh_stars>1-10
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

def train_model(model, criterion, optimizer, scheduler, dataloaders, dataset_sizes, device, num_epochs=25):
    since = time.time()
    training_log = {'epoch':[], 'training_loss':[], 'training_acc':[], 'val_loss':[], 'val_acc':[], 'best_acc':0.0}

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        training_log['epoch'].append(epoch)
        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'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # 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)
            if phase == 'train':
                if scheduler != None:
                    scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            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 == 'train':
                training_log['training_loss'].append(epoch_loss)
                training_log['training_acc'].append(epoch_acc.to('cpu').numpy())
            if phase == 'val':
                training_log['val_loss'].append(epoch_loss)
                training_log['val_acc'].append(epoch_acc.to('cpu').numpy())

        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))
    training_log['best_acc'] = best_acc.to('cpu').numpy()
    training_log['training_loss'] = np.array(training_log['training_loss'])
    training_log['training_acc'] = np.array(training_log['training_acc'])
    training_log['val_loss'] = np.array(training_log['val_loss'])
    training_log['val_acc'] = np.array(training_log['val_acc'])

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model, training_log

def trans_to_cuda(variable):
    if torch.cuda.is_available():
        return variable.cuda()
    else:
        return variable
# Each epoch has a training and validation phase # Set model to training mode # Set model to evaluate mode # Iterate over data. # zero the parameter gradients # forward # track history if only in train # backward + optimize only if in training phase # statistics # deep copy the model # load best model weights