153 lines
4.8 KiB
Python
153 lines
4.8 KiB
Python
# new:
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.optim import lr_scheduler
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import numpy as np
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import torchvision
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from torchvision import datasets, models, transforms
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import matplotlib.pyplot as plt
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import time
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import os
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import copy
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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mean = np.array([0.485, 0.456, 0.406])
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std = np.array([0.229, 0.224, 0.225])
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data_transforms = {
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'train': transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize(mean, std)
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]),
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'val': transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(mean, std)
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])
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}
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#import data
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data_dir = 'data/hymenoptera_data'
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sets = ['train', 'val']
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image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in sets}
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dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=0) for x in sets}
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dataset_sizes = {x: len(image_datasets[x]) for x in sets}
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class_names = image_datasets['train'].classes
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print(f'Classes: {class_names}')
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# # show sample images
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# def imshow(inp, title):
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# """Imshow for Tensor."""
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# inp = inp.numpy().transpose((1, 2, 0))
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# inp = std * inp + mean
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# inp = np.clip(inp, 0, 1)
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# plt.imshow(inp)
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# plt.title(title)
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# plt.show()
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# inputs, classes = next(iter(dataloaders['train']))
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# out = torchvision.utils.make_grid(inputs)
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# imshow(out, title=[class_names[x] for x in classes])
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def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
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since = time.time()
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best_model_wts = copy.deepcopy(model.state_dict())
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best_acc = 0.0
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for epoch in range(num_epochs):
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print(F'Epoch {epoch+1}/{num_epochs}')
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print('-'*11)
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for phase in sets:
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if phase == 'train':
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model.train()
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else:
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model.eval()
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running_loss = 0.0
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running_corrects = 0
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for inputs, labels in dataloaders[phase]:
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inputs = inputs.to(device)
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labels = labels.to(device)
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with torch.set_grad_enabled(phase == 'train'):
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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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loss = criterion(outputs, labels)
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if phase == 'train':
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * inputs.size(0)
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running_corrects += torch.sum(preds == labels.data)
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if phase == 'train':
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scheduler.step()
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epoch_loss = running_loss/dataset_sizes[phase]
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epoch_acc = running_corrects.double()/dataset_sizes[phase]
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print(f'{phase} Loss: {epoch_loss:.4f}, Acc: {epoch_acc:.4f}')
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if phase == 'val' and epoch_acc > best_acc:
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best_acc = epoch_acc
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best_model_wts = copy.deepcopy(model.state_dict())
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print()
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time_elapsed = time.time() - since
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print(f'Training complete in {time_elapsed//(60*60):02.0f}:{(time_elapsed%(60*60)//60):02.0f}:{time_elapsed%60:02.0f}')
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print(f'Best val Acc: {best_acc:.4f}')
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model.load_state_dict(best_model_wts)
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return model
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## transfer learning - finetuning ##
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model = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.DEFAULT) # import pretrained model
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#change last layer
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num_ftrs = model.fc.in_features
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model.fc = nn.Linear(num_ftrs, 2)
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model.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
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# scheduler
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# updates the learning rate
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step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) # every seven epochs: lr = lr*gamma
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model = train_model(model, criterion, optimizer, step_lr_scheduler, num_epochs=50)
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print('-'*50)
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print()
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## transfer learning - feature extractor ##
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model = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.DEFAULT) # import pretrained model # import pretrained model
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for param in model.parameters():
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param.requires_grad = False
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# freezes all layers in beginning
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#change last layer
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num_ftrs = model.fc.in_features
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model.fc = nn.Linear(num_ftrs, 2)
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model.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
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# scheduler
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# updates the learning rate
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step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) # every seven epochs: lr = lr*gamma
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model = train_model(model, criterion, optimizer, step_lr_scheduler, num_epochs=50) |