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resnet34_entire.py
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#%% -------------------------------------- Import Lib --------------------------------------------------------------------
import torch
import torch.nn as nn
import os
import random
import numpy as np
from Helper import train_baseline_model, DataAug, learning_rate_finder, evaluation
from torchvision import models, transforms
from torch.utils.data import Dataset, DataLoader
from sklearn.metrics import f1_score
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
import torch.nn.functional as F
# %% --------------------------------------- Set-Up --------------------------------------------------------------------
SEED = 42
os.environ['PYTHONHASHSEED'] = str(SEED)
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# number of labels
n_classes = 3
# %% -------------------------------------- Data Prep ------------------------------------------------------------------
# load the data
x_train, y_train = np.load("train/x_train.npy"), np.load("train/y_train.npy")
x_valid, y_valid = np.load("train/x_valid.npy"), np.load("train/y_valid.npy")
x_test, y_test = np.load("train/x_test.npy"), np.load("train/y_test.npy")
# one-hot encoding label
#y_train, y_valid, y_test = to_categorical(y_train, num_classes=n_classes), to_categorical(y_valid, num_classes=n_classes), to_categorical(y_test, num_classes=n_classes)
x_train, y_train = shuffle(x_train, y_train) ## shuffle training set
# check shape
#print(x_train.shape, y_train.shape)
#print(x_valid.shape, y_valid.shape)
#print(x_test.shape, y_test.shape)
#%% ------------------------------ DataLoader, Data Augmentation ----------------------------------------------------------
# convert to torch.Tensor
data_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
])
# batch size
batch_train = 128
batch_test = 512
# apply transformation
trainset = DataAug(x_train, y_train, transform = data_transform ,length=len(x_train))
valset = DataAug(x_valid, y_valid, transform = data_transform, length=len(x_valid))
testset = DataAug(x_test, y_test, transform = data_transform, length=len(x_test))
# generate DataLoader
trainloader = DataLoader(trainset, batch_size=batch_train)
valloader = DataLoader(valset, batch_size=batch_test)
testloader = DataLoader(testset, batch_size=batch_test)
# print loader size
#print(len(trainloader.sampler))
#print(len(valloader.sampler))
#print(len(testloader.sampler))
#%% ---------------------------------- Model Architecture ----------------------------------------------------------
model = models.resnet34(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Sequential(
nn.Linear(num_ftrs, n_classes))
for param in model.parameters():
param.requires_grad = True
for param in model.fc.parameters():
param.requires_grad = True
#%% --------------------------------- Learning rate -----------------------------------------------------------------
criterion = nn.CrossEntropyLoss()
loss, rate = learning_rate_finder(model, criterion, min_lr=1e-7, max_lr = 1e-2, iteration = 200, trainloader=trainloader)
#%% --------------------------------- Preparation -----------------------------------------------------------------
model = models.resnet34(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Sequential(
nn.Linear(num_ftrs, n_classes))
for param in model.parameters():
param.requires_grad = True
for param in model.fc.parameters():
param.requires_grad = True
LR = 1e-5
criterion = nn.CrossEntropyLoss()
epochs = 1000
dir = os.path.dirname('Model/')
if not os.path.exists(dir):
os.makedirs(dir)
path ="Model/resnet34_1.pt"
train_losses, val_losses = train_baseline_model(model, criterion, LR, epochs, "train_val", trainloader, valloader, path)
#%% -------------------------- Evaluation ---------------------------------------------------------
# load best model weights
model.load_state_dict(torch.load(path))
TPR_val, FNR_val, score_val = evaluation(model, valloader)
TPR_test, FNR_test, score_test = evaluation(model, testloader)
#%% ---------------------------------------- Learning curve ------------------------------------------------------------
inds = np.arange(1,len(val_losses)+1)
plt.figure()
plt.plot(inds.astype(np.uint8), train_losses, label = "training loss")
plt.plot(inds.astype(np.uint8), val_losses, label = "validation loss")
plt.xlabel("Epoch")
plt.ylabel("Magnitude")
plt.title("Resnet34 model learning curve")
plt.legend(loc='best')
plt.xticks(np.arange(0, max(inds)+2, 3))
plt.show()
#%%
print("Validation set: sensitivity = {:.4f}, specificity = {:.4f}, score = {:.4f}".format(TPR_val, FNR_val, score_val))
print("Testing set: sensitivity = {:.4f}, specificity = {:.4f}, score = {:.4f}".format(TPR_test, FNR_test, score_test))