15_transfer_learning.py

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

mean = np.array([0.5, 0.5, 0.5])
std = np.array([0.25, 0.25, 0.25])

data_transforms = {
 'train': transforms.Compose([
 transforms.RandomResizedCrop(224),
 transforms.RandomHorizontalFlip(),
 transforms.ToTensor(),
 transforms.Normalize(mean, std)
 ]),
 'val': transforms.Compose([
 transforms.Resize(256),
 transforms.CenterCrop(224),
 transforms.ToTensor(),
 transforms.Normalize(mean, std)
 ]),
}

data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
 data_transforms[x])
 for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
 shuffle=True, num_workers=0)
 for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

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


def imshow(inp, title):
 """Imshow for Tensor."""
 inp = inp.numpy().transpose((1, 2, 0))
 inp = std * inp + mean
 inp = np.clip(inp, 0, 1)
 plt.imshow(inp)
 plt.title(title)
 plt.show()


# 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, title=[class_names[x] for x in classes])

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('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)

 # 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':
 optimizer.zero_grad()
 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('{} 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())

 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


#### Finetuning the convnet ####
# Load a pretrained model and reset final fully connected layer.

model = models.resnet18(pretrained=True)
num_ftrs = model.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.fc = nn.Linear(num_ftrs, 2)

model = model.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer = optim.SGD(model.parameters(), lr=0.001)

# StepLR Decays the learning rate of each parameter group by gamma every step_size epochs
# Decay LR by a factor of 0.1 every 7 epochs
# Learning rate scheduling should be applied after optimizer’s update
# e.g., you should write your code this way:
# for epoch in range(100):
# train(...)
# validate(...)
# scheduler.step()

step_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)

model = train_model(model, criterion, optimizer, step_lr_scheduler, num_epochs=25)


#### ConvNet as fixed feature extractor ####
# Here, we need to freeze all the network except the final layer.
# We need to set requires_grad == False to freeze the parameters so that the gradients are not computed in backward()
model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
 param.requires_grad = False

# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)

model_conv = model_conv.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opposed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)

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

model_conv = train_model(model_conv, criterion, optimizer_conv,
 exp_lr_scheduler, num_epochs=25)