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# ------------------------------------------------------------------------
# Libraries
# ------------------------------------------------------------------------
# General libraries
import os
import random
from datetime import datetime
# Deep learning libraries
import torch
from torch import nn
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
# Custom libraries
from utilities import *
from landmarks_datasets import *
from model.deep_learning import *
from model.models import *
# Set random seed
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed(42)
import ssl
ssl._DEFAULT_CIPHERS = 'HIGH:!DH:!aNULL'
def ignore_ssl_certificate_verification():
try:
# Python 3.4+
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
except AttributeError:
# Python 2.x
import requests
from urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
ignore_ssl_certificate_verification()
## -----------------------------------------------------------------------------------------------------------------##
## DATASETS ##
## -----------------------------------------------------------------------------------------------------------------##
datasets_list = ["chest", "cephalo", "hand"]
backbone_list = ["vgg19", "densenet161", "resnext50_32x4d"] #"efficientnet-b5"]
EXPERIMENT_PATH = "downstream_task/landmarks_experiments/backbone_selection"
# Create folder for saving models
if not os.path.exists(EXPERIMENT_PATH):
os.makedirs(EXPERIMENT_PATH)
log_file = f"{EXPERIMENT_PATH}/experiments_results.txt"
NUM_EPOCHS = 200
K_FOLDS = 5
BATCH_SIZE = 2
GRAD_ACC = 8
LR = 1e-5
SIZE = (256, 256)
SIGMA = 5
PATIENCE = GRAD_ACC + 5
EARLY_STOPPING = PATIENCE * 2 + 1
NUM_CHANNELS = 1
ONLY_INFERENCE = False
PIN_MEMORY = True
NUM_WORKERS = 2
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# ---------------------------------------------------------------- CHEST ---------
CHEST_DATASET_PATH = 'datasets/chest'
assert os.path.exists(CHEST_DATASET_PATH), f"Chest dataset path does not exist: {CHEST_DATASET_PATH}, current path: {os.getcwd()}"
CHEST_NUM_LANDMARKS = 6
CHEST_SIZE = SIZE
CHEST_SIGMA = SIGMA
chest_train_dataset = Chest(prefix=CHEST_DATASET_PATH, phase='train', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
chest_val_dataset = Chest(prefix=CHEST_DATASET_PATH, phase='validate', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
chest_test_dataset = Chest(prefix=CHEST_DATASET_PATH, phase='test', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
chest_train_val_dataset = torch.utils.data.ConcatDataset([chest_train_dataset, chest_val_dataset])
print(f"CHEST: {len(chest_train_dataset)} | {len(chest_val_dataset)} | {len(chest_test_dataset)}")
# ---------------------------------------------------------------- CEPHALOMETRIC ---------
CEPHALOMETRIC_DATASET_PATH = 'datasets/cephalo'
assert os.path.exists(CEPHALOMETRIC_DATASET_PATH), f"Cephalometric dataset path does not exist: {CEPHALOMETRIC_DATASET_PATH}, current path: {os.getcwd()}"
CEPHALOMETRIC_NUM_LANDMARKS = 19
CEPHALOMETRIC_SIZE = SIZE
CEPHALOMETRIC_SIGMA = SIGMA
cephalo_train_dataset = Cephalo(prefix=CEPHALOMETRIC_DATASET_PATH, phase='train', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
cephalo_val_dataset = Cephalo(prefix=CEPHALOMETRIC_DATASET_PATH, phase='validate', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
cephalo_test_dataset = Cephalo(prefix=CEPHALOMETRIC_DATASET_PATH, phase='test', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
cephalo_train_val_dataset = torch.utils.data.ConcatDataset([cephalo_train_dataset, cephalo_val_dataset])
print(f"CEPHALO: {len(cephalo_train_dataset)} | {len(cephalo_val_dataset)} | {len(cephalo_test_dataset)}")
# ---------------------------------------------------------------- HAND ---------
HAND_DATASET_PATH = 'datasets/hand'
assert os.path.exists(HAND_DATASET_PATH), f"Hand dataset path does not exist: {HAND_DATASET_PATH}, current path: {os.getcwd()}"
HAND_NUM_LANDMARKS = 37
HAND_SIZE = SIZE
HAND_SIGMA = SIGMA
hand_train_dataset = Hand(prefix=HAND_DATASET_PATH, phase='train', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
hand_val_dataset = Hand(prefix=HAND_DATASET_PATH, phase='validate', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
hand_test_dataset = Hand(prefix=HAND_DATASET_PATH, phase='test', size=SIZE, num_channels=NUM_CHANNELS, sigma=SIGMA)
hand_train_val_dataset = torch.utils.data.ConcatDataset([hand_train_dataset, hand_val_dataset])
print(f"HAND: {len(hand_train_dataset)} | {len(hand_val_dataset)} | {len(hand_test_dataset)}")
## -----------------------------------------------------------------------------------------------------------------##
## TRAINING ##
## -----------------------------------------------------------------------------------------------------------------##
for i in datasets_list:
print(f"\n\n\n {datetime.now()} ---------------------- {i.upper()} -------------------------------------------")
print(f"SIZE: {SIZE} | BATCH: {BATCH_SIZE} | GRAD ACC: {GRAD_ACC} | SIGMA: {SIGMA} | LR: {LR} | CHANNELS: {NUM_CHANNELS}")
if i == "chest":
NUM_LANDMARKS = CHEST_NUM_LANDMARKS
dataset_name = i
training_dataset = chest_train_val_dataset
elif i == "hand":
NUM_LANDMARKS = HAND_NUM_LANDMARKS
dataset_name = i
training_dataset = hand_train_val_dataset
elif i == "cephalo":
NUM_LANDMARKS = CEPHALOMETRIC_NUM_LANDMARKS
dataset_name = i
training_dataset = cephalo_train_val_dataset
res_file = open(log_file, 'a')
print(f"\n\n ----------------------------------------- {dataset_name.upper()} DATASET ------------------------", file=res_file)
res_file.close()
# -------------------------------------------- SEGMENTATION MODELS -------------
useHEATMAPS = True
pretrained = "imagenet"
for backbone in backbone_list:
model = smpUnet(
encoder_name=backbone,
encoder_weights="imagenet",
in_channels=NUM_CHANNELS,
classes=NUM_LANDMARKS
).to(device)
model_name = model.__class__.__name__
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(params=model.parameters(), lr=LR)
scheduler = ReduceLROnPlateau(optimizer, patience=PATIENCE, factor=0.5)
res_file = open(log_file, 'a')
print(f"\n\n --------- Model: {model_name}_{backbone} | Dataset: {dataset_name} | Batch: {BATCH_SIZE} | Sigma: {SIGMA} | Size: {SIZE}", file=res_file)
res_file.close()
save_model_path = generate_save_model_path(EXPERIMENT_PATH, model_name, dataset_name, SIGMA, SIZE, pretrained, backbone)
k_fold_train_and_validate(model, device, training_dataset, optimizer, scheduler, loss_fn, NUM_EPOCHS, EARLY_STOPPING, BATCH_SIZE, GRAD_ACC,
NUM_LANDMARKS, SIGMA, save_model_path, log_file, K_FOLDS, onlyInference=ONLY_INFERENCE)
free_gpu_cache()
del model, loss_fn, optimizer, scheduler |