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"""
Linear and non-linear image classification tasks with and w/o frozen image encoders
"""
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader, Dataset
from PIL import Image
import numpy as np
from sklearn.model_selection import train_test_split
import torchvision.datasets as datasets
import argparse
from tqdm import tqdm
import os
import random
import torch.nn.functional as F
seed = 42
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
weight_collections = {
"resnet50": {
"lvm-med-resnet": "./lvm_med_weights/lvmmed_resnet.torch",
}
}
def eval(net, dataloader, device, criterion, num_samples):
# Evaluate the model on the validation set
val_loss = 0.0
val_acc = 0.0
net.eval()
with tqdm(total=len(dataloader), desc='Validation round', unit=' img') as pbar:
for inputs, labels in dataloader:
inputs = inputs.to(device)
labels = labels.to(device)
with torch.no_grad():
outputs = net(inputs)
loss = criterion(outputs, labels)
val_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
val_acc += torch.sum(preds == labels.data)
pbar.update(inputs.shape[0])
val_loss /= num_samples
val_acc /= num_samples
net.train()
return val_loss, val_acc
def TrainingTesting(cfg, numtry, pretrained_weight_name, data_path, num_classes, data_tranform, device, solver, name_weights,
frozen_encoder, architecture_type, number_epoch=50, learning_rate=0.001, batch_size=32, test_mode='best_valid',
valid_rate=0.2):
# Load the datasets
train_dir = data_path + "/Training"
test_dir = data_path + "/Testing"
if frozen_encoder:
checkpoint_dir = cfg.base.best_valid_model_checkpoint + cfg.base.dataset_name + "_" + architecture_type + "_" + name_weights + "_frozen/"
else:
checkpoint_dir = cfg.base.best_valid_model_checkpoint + cfg.base.dataset_name + "_" + architecture_type + "_" + name_weights + "_non_frozen/"
CHECK_FOLDER = os.path.isdir(checkpoint_dir)
if not CHECK_FOLDER:
os.makedirs(checkpoint_dir)
print("created folder: ", checkpoint_dir)
train_dataset = datasets.ImageFolder(root=train_dir, transform=data_tranform)
test_dataset = datasets.ImageFolder(root=test_dir, transform=data_tranform)
print ("valid size is {}".format(valid_rate))
# Split the training dataset into training and validation sets
train_indices, val_indices = train_test_split(list(range(len(train_dataset))), test_size=valid_rate, random_state=42)
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(train_indices)
val_sampler = torch.utils.data.sampler.SubsetRandomSampler(val_indices)
loader_args = dict(num_workers=10, pin_memory=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler, **loader_args)
val_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=val_sampler, **loader_args)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, **loader_args)
n_train = len(train_indices)
# Define the ResNet50 model
model = torchvision.models.resnet50(pretrained=True)
# Freeze the layers of the ResNet50 model
if frozen_encoder:
print ("Frozen encoder")
for param in model.parameters():
param.requires_grad = False
num_ftrs = model.fc.in_features
# define linear or non-linear architecture
if architecture_type == '1-fcn':
print ("Using single fully-connected layer")
model.fc = nn.Linear(num_ftrs, num_classes)
elif architecture_type == "fcns":
print("Using several fully-connected layers")
if cfg.base.dataset_name == 'brain':
model.fc = nn.Sequential(
nn.Linear(num_ftrs, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, num_classes))
elif cfg.base.dataset_name == 'fgadr':
model.fc = nn.Sequential(
nn.Linear(num_ftrs, 512),
nn.ReLU(),
nn.Linear(512, 128),
nn.ReLU(),
nn.Linear(128, num_classes))
else:
print(">>> Not implemented for selected datasets")
exit()
else:
print (">>> No available option for achitecture. Please check 'help' with --linear option")
exit()
pretrained_weight = torch.load(weight_collections["resnet50"][pretrained_weight_name], map_location=device)
model.load_state_dict(pretrained_weight, strict=False)
print("Loaded pretrained-weight of ", pretrained_weight_name)
model = model.to(device)
criterion = nn.CrossEntropyLoss()
if solver == "sgd":
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)
elif solver == "adam":
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08,
weight_decay=0.)
if cfg.base.dataset_name == 'fgadr':
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08,
weight_decay=0.005)
else:
print ("Non-available solver")
exit()
## ------------ Train the model ------------
print(" ------------ Training ------------ ")
num_epochs = number_epoch
best_acc_val = 0.
for epoch in range(num_epochs):
print(f"Epoch {epoch + 1}:")
train_loss = 0.0
train_acc = 0.0
# Train the model on the training set
model.train()
with tqdm(total=n_train, desc=f'Epoch {epoch + 1}/{num_epochs}', unit='img') as pbar:
for inputs, labels in train_loader:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
train_acc += torch.sum(preds == labels.data)
# updating progressing bar
pbar.update(inputs.shape[0])
pbar.set_postfix(**{'loss (batch)': loss.item()})
# Print the results for this epoch
train_loss /= len(train_indices)
train_acc /= len(train_indices)
num_samples = len(val_indices)
num_samples_test = len(test_dataset)
print(" \n >>> Evaluation ")
val_loss, val_acc = eval(model, val_loader, device, criterion, num_samples)
if val_acc >= best_acc_val:
checkpoint = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(checkpoint, checkpoint_dir
+ name_weights + "_" + pretrained_weight_name + "_" + str(numtry) + ".pth")
print("Saved checkpoint at epoch ", epoch + 1)
best_acc_val = val_acc
print(f"Training Loss: {train_loss:.4f}\t Training Accuracy: {train_acc:.4f}")
print(f"Val Loss: {val_loss:.4f}\tVal Accuracy: {val_acc:.5f}")
# print model at last epochs
checkpoint = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(checkpoint, checkpoint_dir
+ name_weights + "_" + pretrained_weight_name + "_last_" + str(numtry) + ".pth")
print("Saved checkpoint at last epoch ", epoch + 1)
## ------------ Test the model ------------
print("------ Testing ------")
if test_mode == "best_valid":
print("Loading best models at {}".format(checkpoint_dir))
ckp = torch.load(checkpoint_dir
+ name_weights + "_" + pretrained_weight_name + "_" + str(numtry) + ".pth")
else:
print("Loading models at last epochs {}".format(checkpoint_dir))
ckp = torch.load(checkpoint_dir
+ name_weights + "_" + pretrained_weight_name + "_last_" + str(numtry) + ".pth")
model.load_state_dict(ckp['state_dict'])
num_samples_test = len(test_dataset)
test_loss, test_acc = eval(model, test_loader, device, criterion, num_samples_test)
print(f"Test Loss: {test_loss:.4f}\tTest Accuracy: {test_acc:.5f}")
return test_acc
def inference(numtry, device, cfg, data_path, data_tranform, name_weights, pretrained_weight_name,
frozen_encoder, architecture_type, num_classes):
if frozen_encoder:
checkpoint_dir = cfg.base.best_valid_model_checkpoint + cfg.base.dataset_name + "_" + architecture_type + "_" + name_weights + "_frozen/"
else:
checkpoint_dir = cfg.base.best_valid_model_checkpoint + cfg.base.dataset_name + "_" + architecture_type + "_" + name_weights + "_non_frozen/"
loader_args = dict(num_workers=10, pin_memory=True)
test_dir = data_path + "/Testing"
test_dataset = datasets.ImageFolder(root=test_dir, transform=data_tranform)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, **loader_args)
model = torchvision.models.resnet50(pretrained=True)
# Freeze the layers of the ResNet50 model
if frozen_encoder:
print ("Frozen encoder")
for param in model.parameters():
param.requires_grad = False
num_ftrs = model.fc.in_features
if architecture_type == '1-fcn':
print ("Using single fully-connected layer")
model.fc = nn.Linear(num_ftrs, num_classes)
elif architecture_type == "fcns":
print("Using several fully-connected layers")
if cfg.base.dataset_name == 'brain':
model.fc = nn.Sequential(
nn.Linear(num_ftrs, 512),
nn.ReLU(),
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, num_classes))
elif cfg.base.dataset_name == 'fgadr':
model.fc = nn.Sequential(
nn.Linear(num_ftrs, 512),
nn.ReLU(),
nn.Linear(512, 128),
nn.ReLU(),
nn.Linear(128, num_classes))
else:
print(">>> Not implemented for selected datasets")
exit()
else:
print (">>> No available option for achitecture. Please check 'help' with --linear option")
exit()
model = model.to(device)
print("Loading best models at {}".format(checkpoint_dir))
ckp = torch.load(checkpoint_dir
+ name_weights + "_" + pretrained_weight_name + "_" + str(numtry) + ".pth")
model.load_state_dict(ckp['state_dict'])
num_samples_test = len(test_dataset)
criterion = nn.CrossEntropyLoss()
test_loss, test_acc = eval(model, test_loader, device, criterion, num_samples_test)
print(f"Test Loss: {test_loss:.4f}\tTest Accuracy: {test_acc:.5f}")
return test_acc
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def train_R50(yml_args, cfg):
if cfg.base.dataset_name == 'brain':
data_path = cfg.dataloader.data_path
num_classes = 4
data_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
elif cfg.base.dataset_name == 'fgadr':
data_path = cfg.dataloader.data_path
num_classes = 5
data_transforms = transforms.Compose([
transforms.RandomCrop(size=(480, 480)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomAutocontrast(p=0.2),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
else:
print (">>> No available datasets")
exit()
print ("Using dataset {}".format(cfg.base.dataset_name))
list_acc = []
name_weight = cfg.base.original_checkpoint + "_output"
cuda_string = 'cuda:' + cfg.base.gpu_id
devices = torch.device(cuda_string if torch.cuda.is_available() else 'cpu')
if not yml_args.use_test_mode:
# Training model with three trial times
for numtry in range(3):
print ("*****"*3 + "\n" + "Trial", numtry)
test_acc = TrainingTesting(cfg = cfg, numtry=numtry, pretrained_weight_name=cfg.base.original_checkpoint, data_path = data_path,
num_classes = num_classes,
data_tranform = data_transforms,
device=devices,
solver=cfg.train.solver,
name_weights=name_weight, frozen_encoder=cfg.base.frozen_eval,
number_epoch=cfg.train.num_epochs, architecture_type=cfg.base.model,
learning_rate=cfg.train.learning_rate, batch_size=cfg.train.train_batch_size,
test_mode=cfg.base.test_mode,
valid_rate = cfg.base.valid_rate)
list_acc.append(test_acc.to('cpu'))
print("==============================================================================")
print ("*****"*3 + "\n")
print("Mean Accuracy: ", np.mean(list_acc))
print("Standard Deviation: ", np.std(list_acc))
else:
# Evaluate model with three weights
for numtry in range(3):
print ("*****"*3 + "\n" + "weight", numtry+1)
test_acc = inference(numtry = numtry, device = devices, cfg = cfg, data_path = data_path, data_tranform=data_transforms,
name_weights=name_weight, pretrained_weight_name=cfg.base.original_checkpoint,
frozen_encoder=cfg.base.frozen_eval, architecture_type=cfg.base.model, num_classes=num_classes)
list_acc.append(test_acc.to('cpu'))
print ("*****"*3 + "\n")
print("Mean Accuracy: ", np.mean(list_acc))
print("Standard Deviation: ", np.std(list_acc))
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