fineTuning_02.py

# This version will construct a feature extractor from each specified pre-tranined CNN
# 
# https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html
# Learn how to use pre-trained CNN for feature extraction

# To reshape each CNN model for the target application, print the model (print(model_ft)) and observe where the output is from 
# resnet: model.fc = nn.Linear(512, num_classes)
# Alexnet: model.classifier[6] = nn.Linear(4096,num_classes)
# Vgg: model.classifier[6] = nn.Linear(4096,num_classes)
# Squeezenet: model.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
# Densenet: model.classifier = nn.Linear(1024, num_classes)
# Inception v3: model.AuxLogits.fc = nn.Linear(768, num_classes)
#               model.fc = nn.Linear(2048, num_classes)
#
#
# https://towardsdatascience.com/visualizing-convolution-neural-networks-using-pytorch-3dfa8443e74e
# Learn how to extract feature extractor from pretrained CNN models (used vgg as an example)


# Finally, notice that inception_v3 requires the input size to be (299,299), 
# whereas all of the other models expect (224,224).
#%%
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)

#%%
data_dir = "/home/hchen/MEGA/fineTuning/hymenoptera_data"

# Models to choose from [resnet, alexnet, vgg, squeezenet, densenet, inception]
model_list = ["resnet", "alexnet", "vgg", "squeezenet", "densenet", "inception"]
model_name = model_list[2]

# Number of classes in the dataset
num_classes = 2

# Batch size for training (change depending on how much memory you have)
batch_size = 8

# Number of epochs to train for
num_epochs = 15

# Flag for feature extracting. When False, we finetune the whole model,
#   when True we only update the reshaped layer params
feature_extract = True

def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False):
    since = time.time()

    val_acc_history = []

    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)

                # zero the parameter gradients
                optimizer.zero_grad()
            # 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'):
                    # Get model outputs and calculate loss
                    # Special case for inception because in training it has an auxiliary output. In train
                    #   mode we calculate the loss by summing the final output and the auxiliary output
                    #   but in testing we only consider the final output.
                    if is_inception and phase == 'train':
                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
                        outputs, aux_outputs = model(inputs)
                        loss1 = criterion(outputs, labels)
                        loss2 = criterion(aux_outputs, labels)
                        loss = loss1 + 0.4*loss2
                    else:
                        outputs = model(inputs)
                        loss = criterion(outputs, labels)

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

                    # 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)

            epoch_loss = running_loss / len(dataloaders[phase].dataset)
            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
            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 == 'val':
                val_acc_history.append(epoch_acc)

        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, val_acc_history

def set_parameter_requires_grad(model, feature_extracting):
    if feature_extracting:
        for param in model.parameters():
            param.requires_grad = False

def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
    # Initialize these variables which will be set in this if statement. Each of these
    #   variables is model specific.
    model_ft = None
    input_size = 0

    if model_name == "resnet":
        """ Resnet18
        """
        model_ft = models.resnet18(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "alexnet":
        """ Alexnet
        """
        model_ft = models.alexnet(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "vgg":
        """ VGG11_bn
        """
        model_ft = models.vgg11_bn(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "squeezenet":
        """ Squeezenet
        """
        model_ft = models.squeezenet1_0(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
        model_ft.num_classes = num_classes
        input_size = 224

    elif model_name == "densenet":
        """ Densenet
        """
        model_ft = models.densenet121(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier.in_features
        model_ft.classifier = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "inception":
        """ Inception v3
        Be careful, expects (299,299) sized images and has auxiliary output
        """
        model_ft = models.inception_v3(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        # Handle the auxilary net
        num_ftrs = model_ft.AuxLogits.fc.in_features
        model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
        # Handle the primary net
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs,num_classes)
        input_size = 299

    else:
        print("Invalid model name, exiting...")
        exit()

    return model_ft, input_size

class FeatureExtractor(nn.Module):
  def __init__(self, model_name,use_pretrained = True):
    super(FeatureExtractor, self).__init__()
    self.model_name = model_name

    if model_name == "vgg":
        model = models.vgg11_bn(pretrained=use_pretrained)
        # Extract VGG-16 Feature Layers
        self.features = list(model.features)
        self.features = nn.Sequential(*self.features)
            # Extract VGG-16 Average Pooling Layer
        self.pooling = model.avgpool
            # Convert the image into one-dimensional vector
        self.flatten = nn.Flatten()
            # Extract the first part of fully-connected layer from VGG16
        self.fc1 = model.classifier[0]
        self.relu1 = model.classifier[1] #nn.ReLU(inplace=True)
        self.dropout1 = model.classifier[2] #nn.Dropout(p=0.5,inplace=False)   
        self.fc2=model.classifier[3] #nn.Linear(in_features=4096, out_features=4096, bias=True)     
        self.relu2 = model.classifier[4] #nn.ReLU(inplace=True)
        self.dropout2 = model.classifier[5] #nn.Dropout(p=0.5,inplace=False)   

  
  def forward(self, x):
		# It will take the input 'x' until it returns the feature vector called 'out'
    if self.model_name == "vgg":
        out = self.features(x)
        out = self.pooling(out)
        out = self.flatten(out)
        out = self.fc1(out) 
        out = self.relu1(out)
        out = self.dropout1(out)
        out = self.fc2(out) 
        out = self.relu2(out)
        out = self.dropout2(out)
    
    return out 

class NewClassifier(nn.Module):
    def __init__(self, featureExtractor):
        super(NewClassifier, self).__init__()
        self.features = featureExtractor
        self.fc = nn.Linear(in_features=4096, out_features=2, bias=True)    
    def forward(self, x):
		# It will take the input 'x' until it returns the feature vector called 'out'
        out = self.features(x)
        out = self.fc(out) 
    
        return out 

def initialize_new_model(model_name, num_classes, feature_extract, use_pretrained=True):
    # Initialize these variables which will be set in this if statement. Each of these
    #   variables is model specific.
    featureExtractor = FeatureExtractor(model_name,use_pretrained)
    set_parameter_requires_grad(featureExtractor, feature_extract)
    model_ft = NewClassifier(featureExtractor)
    input_size = 224

    if model_name == "resnet":
        """ Resnet18
        """
        #model_ft = models.resnet18(pretrained=use_pretrained)
        #set_parameter_requires_grad(model_ft, feature_extract)
        #num_ftrs = model_ft.fc.in_features
        #model_ft.fc = nn.Linear(num_ftrs, num_classes)
        #input_size = 224

    elif model_name == "alexnet":
        """ Alexnet
        """
        #model_ft = models.alexnet(pretrained=use_pretrained)
        #set_parameter_requires_grad(model_ft, feature_extract)
        #num_ftrs = model_ft.classifier[6].in_features
        #model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        #input_size = 224

    elif model_name == "vgg":
        """ VGG11_bn
        """
        #model_ft = models.vgg11_bn(pretrained=use_pretrained)
        #set_parameter_requires_grad(model_ft, feature_extract)
        #num_ftrs = model_ft.classifier[6].in_features
        #model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        #input_size = 224

    elif model_name == "squeezenet":
        """ Squeezenet
        """
        #model_ft = models.squeezenet1_0(pretrained=use_pretrained)
        #set_parameter_requires_grad(model_ft, feature_extract)
        #model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
        #model_ft.num_classes = num_classes
        #input_size = 224

    elif model_name == "densenet":
        """ Densenet
        """
        #model_ft = models.densenet121(pretrained=use_pretrained)
        #set_parameter_requires_grad(model_ft, feature_extract)
        #num_ftrs = model_ft.classifier.in_features
        #model_ft.classifier = nn.Linear(num_ftrs, num_classes)
        #input_size = 224

    elif model_name == "inception":
        """ Inception v3
        Be careful, expects (299,299) sized images and has auxiliary output
        """
        #model_ft = models.inception_v3(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        # Handle the auxilary net
        #num_ftrs = model_ft.AuxLogits.fc.in_features
        #model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
        # Handle the primary net
        #num_ftrs = model_ft.fc.in_features
        #model_ft.fc = nn.Linear(num_ftrs,num_classes)
        input_size = 299

    else:
        print("Invalid model name, exiting...")
        exit()

    return model_ft, input_size
# Initialize the model for this run

model_new,input_size = initialize_new_model(model_name,num_classes,feature_extract,use_pretrained=True)
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(input_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(input_size),
        transforms.CenterCrop(input_size),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

print("Initializing Datasets and Dataloaders...")

# Create training and validation datasets
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
# Create training and validation dataloaders
dataloaders_dict = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=4) for x in ['train', 'val']}

# Detect if we have a GPU available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Send the model to GPU
model_new = model_new.to(device)
# Gather the parameters to be optimized/updated in this run. If we are
#  finetuning we will be updating all parameters. However, if we are
#  doing feature extract method, we will only update the parameters
#  that we have just initialized, i.e. the parameters with requires_grad
#  is True.
params_to_update = model_new.parameters()
print("Params to learn:")
if feature_extract:
    params_to_update = []
    for name,param in model_new.named_parameters():
        if param.requires_grad == True:
            params_to_update.append(param)
            print("\t",name)
else:
    for name,param in model_new.named_parameters():
        if param.requires_grad == True:
            print("\t",name)

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(params_to_update, lr=0.001, momentum=0.9)

# Setup the loss fxn
criterion = nn.CrossEntropyLoss()

# Train and evaluate
model_new, hist = train_model(model_new, dataloaders_dict, criterion, optimizer_ft, num_epochs=num_epochs, is_inception=(model_name=="inception"))

# Comparison with Model Trained from Scratch

# Initialize the non-pretrained version of the model used for this run
scratch_model,_ = initialize_new_model(model_name, num_classes, feature_extract=False, use_pretrained=False)
scratch_model = scratch_model.to(device)
scratch_optimizer = optim.SGD(scratch_model.parameters(), lr=0.001, momentum=0.9)
scratch_criterion = nn.CrossEntropyLoss()
_,scratch_hist = train_model(scratch_model, dataloaders_dict, scratch_criterion, scratch_optimizer, num_epochs=num_epochs, is_inception=(model_name=="inception"))

# Plot the training curves of validation accuracy vs. number
#  of training epochs for the transfer learning method and
#  the model trained from scratch
ohist = []
shist = []

ohist = [h.cpu().numpy() for h in hist]
shist = [h.cpu().numpy() for h in scratch_hist]

plt.title("Validation Accuracy vs. Number of Training Epochs")
plt.xlabel("Training Epochs")
plt.ylabel("Validation Accuracy")
plt.plot(range(1,num_epochs+1),ohist,label="Pretrained")
plt.plot(range(1,num_epochs+1),shist,label="Scratch")
plt.ylim((0,1.))
plt.xticks(np.arange(1, num_epochs+1, 1.0))
plt.legend()
plt.savefig("vgg_new.png")
plt.show()
plt.pause(5)
plt.close()


# %%