تصنيف الوجبات السريعة باستخدام نقل التعلم وپاي تورچ

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

PATH = "../data/Fast Food Classification V2/"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# make sure my GPU is detected.
print(device)

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])
    ]),
    'Valid': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

image_datasets = {
    x: datasets.ImageFolder(os.path.join(PATH, x),
                            data_transforms[x]) for x in ['Train', 'Valid']
}
dataloaders = {
    x: torch.utils.data.DataLoader(image_datasets[x], 
                                   batch_size=32,
                                   shuffle=True, 
                                ) for x in ['Train', 'Valid']
}
dataset_sizes = {x: len(image_datasets[x]) for x in ['Train', 'Valid']}


class_names = image_datasets['Train'].classes
print(classes)

>>>
['Baked Potato',
 'Burger',
 'Crispy Chicken',
 'Donut',
 'Fries',
 'Hot Dog',
 'Pizza',
 'Sandwich',
 'Taco',
 'Taquito']

# create a function image show

def imshow(inp, title=None):
    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)
    plt.pause(0.001)

# Get a batch of training data
inputs, classes = next(iter(dataloaders['Train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out)

def train_model(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', 'Valid']:
            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 == 'Valid' 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 Valid Acc: {best_acc:4f}')

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

model_1 = models.resnet18(pretrained=True)
num_ftrs = model_1.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_1.fc = nn.Linear(num_ftrs, len(class_names))

model_1 = model_1.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_sgd = optim.SGD(model_1.parameters(), lr=0.001, momentum=0.9)
optimizer_adam = optim.Adam(model_1.parameters(), lr=0.001)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_adam, step_size=7, gamma=0.1)


model_resnetft = train_model(model_1, criterion, optimizer_adam, exp_lr_scheduler,
                       num_epochs=15)
 
 output >>>
 Epoch 0/14
----------
Train Loss: 1.3397 Acc: 0.5660
Valid Loss: 1.0503 Acc: 0.6691
     .
     .
     .
     continues
     .
     .
     .
 Epoch 14/14
----------
Train Loss: 0.4054 Acc: 0.8709
Valid Loss: 0.4723 Acc: 0.8600

Training complete in 27m 23s
Best Valid Acc: 0.867714

def visualize_model(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['Valid']):
            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]]}')
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)
        
        
 # Visualize model
 visualize_model(model_1)