task3_functions.py

import copy
import time
import torch
import numpy as np
import matplotlib.pyplot as plt

from torchvision import transforms
from numpy.random import default_rng

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}


# convenience functions
def view_grid(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
        

def train_model(device, dataloaders, dataset_sizes, model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

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

    for epoch in range(num_epochs):
        print(f'Epoch {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':
                scheduler.step()

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

            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
    print(f'Best val Acc: {best_acc:4f}')

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


def visualize_model(device, dataloaders, class_names, model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title(f'predicted: {class_names[preds[j]]}')
                view_grid(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)


def explore_wrong_5x5_rgb(dataloader, model, device, class_labels=None, seed=None, replace=False):

    model.eval()
    rng = default_rng(seed)
    all_wrong = torch.empty(0, dtype=torch.int64, device=device)
    preds = torch.empty(0, dtype=torch.int64, device=device)
    gtruths = torch.empty(0, dtype=torch.int64, device=device)
    for X, y in dataloader:
        X, y = X.to(device), y.to(device)
        pred = model(X).argmax(1)
        wrong = pred != y
        wrong_ixs = torch.argwhere(wrong).flatten()
        for ix in wrong_ixs:
            all_wrong = torch.cat((all_wrong, X[ix, ...][None, ...]))
            preds = torch.cat((preds, torch.tensor([pred[ix]]).to(device)))
            gtruths = torch.cat((gtruths, torch.tensor([y[ix]]).to(device)))

    example_ixs = rng.choice(range(len(gtruths)), 25, replace=replace)

    fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(14, 14))
    fig.tight_layout()
    for i, ix in enumerate(example_ixs):
        X = all_wrong[ix]
        y = gtruths[ix]
        y_guess = preds[ix]
        if class_labels:
            true = class_labels[y]
            guess = class_labels[y_guess]
        else:
            true = str(int(y))
            guess = str(int(y_guess))
        ax = axes.flatten()[i]
        ax.set_title(f'True:{true}, Guess:{guess}')
        im = X.squeeze().cpu().numpy()
        im = np.moveaxis(im, 0, -1)
        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])
        im = std * im + mean
        im = np.clip(im, 0, 1)
        ax.imshow(im)
    model.train()