vanilla_resnet.py

# Ref: https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html

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
from preprocess import mean, std, preprocess_input_function
from resnet_features import resnet50_features
print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)

class ResnetTester(nn.Module):
    def __init__(self, model):
        super(ResnetTester, self).__init__()
        
        self.layers = model
        self.classifier = nn.Linear(2048*7*7, 200)

    def forward(self, input):
        features_in = self.layers(input)
        features_in = features_in.view(features_in.shape[0], -1)
        return self.classifier(features_in)

torch.manual_seed(0)

offline_aug = False
online_aug = True

print("offline_aug = ", offline_aug)
print("online_aug = ", online_aug)

Adam = False

# Top level data directory. Here we assume the format of the directory conforms
#   to the ImageFolder structure
data_dir = "./datasets/CUB_200_2011/"

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

# Number of classes in the dataset
num_classes = 200

# Batch size for training (change depending on how much memory you have)
batch_size = 25

# Number of epochs to train for
num_epochs = 30

# where to save the model
model_dir = './saved_models/' + model_name + '/'

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


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

    test_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 test phase
        for phase in ['train', 'test']:
            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'):
                    # 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 == 'test' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
            if phase == 'test':
                test_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 test Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model, test_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":
        print("Using ResNet50 iNat")
        model_ft = resnet50_features(inat=True, pretrained=True) #
        #inat_model = torch.load("./pretrained_models/BBN.iNaturalist2017.res50.90epoch.best_model.pth")
        #model_dict = model_ft.state_dict()
        #pretrained_dict = inat_model#.state_dict()
        # 1. filter out unnecessary keys
        #pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 2. overwrite entries in the existing state dict
        # PREVIOUS RESULT MAY NOT HAVE ACTUALLY USED iNAT
        #model_dict.update(pretrained_dict) 
        #model_dict['fc'] = nn.Linear(200, num_classes)
        # 3. load the new state dict
        #missing, unexpected = model_ft.load_state_dict(pretrained_dict, strict=False)
        model_ft = ResnetTester(model_ft)

        #num_ftrs = model_ft.classifier.in_features
        set_parameter_requires_grad(model_ft, feature_extract)
        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":
        """ VGG19
        """
        print("Using VGG19, no bn")
        model_ft = models.vgg19(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
        """
        print("Using densenet161")
        model_ft = models.densenet161(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_ft, input_size = initialize_model(model_name, num_classes, feature_extract, use_pretrained=True)

# Print the model we just instantiated
print(model_ft)

# 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_ft = model_ft.to(device)

# Data augmentation and normalization for training
# Just normalization for test
if online_aug:
    data_transforms = {
        'train': transforms.Compose([
            transforms.RandomAffine(degrees=(-25, 25), shear=15),
            transforms.RandomHorizontalFlip(),
            transforms.Resize(size=(input_size, input_size)),
            transforms.ToTensor(),
            transforms.Normalize(mean=mean, std=std)
        ]),
        'test': transforms.Compose([
            transforms.Resize(size=(input_size, input_size)),
            transforms.ToTensor(),
            transforms.Normalize(mean=mean, std=std)
        ]),
    }
else:
    data_transforms = {
        'train': transforms.Compose([
            # transforms.RandomAffine(degrees=(-25, 25), shear=15),
            # transforms.RandomHorizontalFlip(),
            transforms.Resize(size=(input_size, input_size)),
            transforms.ToTensor(),
            transforms.Normalize(mean=mean, std=std)
        ]),
        'test': transforms.Compose([
            transforms.Resize(size=(input_size, input_size)),
            transforms.ToTensor(),
            transforms.Normalize(mean=mean, std=std)
        ]),
    }

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

# Create training and test datasets
if offline_aug:
    image_datasets = {'train': datasets.ImageFolder(os.path.join(data_dir, 'train_augmented'), data_transforms['train']),
                      'test': datasets.ImageFolder(os.path.join(data_dir, 'test'), data_transforms['test'])}
else:
    image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'test']}

# Create training and test dataloaders
dataloaders_dict = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=4) for x in ['train', 'test']}

# 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_ft.parameters()
print("Params to learn:")
if feature_extract:
    params_to_update = []
    for name,param in model_ft.named_parameters():
        if param.requires_grad == True:
            params_to_update.append(param)
            print("\t",name)
else:
    for name,param in model_ft.named_parameters():
        if param.requires_grad == True:
            print("\t",name)

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

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

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

torch.save(obj=model_ft.state_dict(), f=model_dir + "resnet50_pretrained_{}.pth".format(max(hist)))
# Initialize the non-pretrained version of the model used for this run
scratch_model,_ = initialize_model(model_name, num_classes, feature_extract=False, use_pretrained=False)
scratch_model = scratch_model.to(device)
if Adam:
    scratch_optimizer = optim.Adam(scratch_model.parameters())
else:
    scratch_optimizer = optim.SGD(scratch_model.parameters(), lr=0.001, momentum=0.9)
scratch_criterion = nn.CrossEntropyLoss()
model,scratch_hist = train_model(scratch_model, dataloaders_dict, scratch_criterion, scratch_optimizer, num_epochs=num_epochs, is_inception=(model_name=="inception"))
torch.save(obj=model.state_dict(), f=model_dir + "resnet50_scratch_{}.pth".format(max(scratch_hist)))

# Plot the training curves of test 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("test Accuracy vs. Number of Training Epochs")
plt.xlabel("Training Epochs")
plt.ylabel("test 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('./fig.png')