def main(
model,
config=None,
comment="No comment",
checkpoint=None,
):
if checkpoint is not None:
print("...Load checkpoint from {}".format(checkpoint))
checkpoint = torch.load(checkpoint)
model.load_state_dict(checkpoint['state_dict'])
print("...Checkpoint loaded")
# Checking cuda
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logging.info("Using device: {} ".format(device))
# Convert to suitable device
model = model.to(device)
print("Number of parameters: ", sum(p.numel() for p in model.parameters()))
logging.info("Model created...")
# using parsed configurations to create a dataset
num_of_class = len(cfg["data"]["label_dict"])
# Create dataset
train_loader, valid_loader, test_loader = get_data_loader(cfg)
logging.info("Dataset and Dataloaders created")
# create a metric for evaluating
train_metrics = metrics.Metrics(cfg["train"]["metrics"])
val_metrics = metrics.Metrics(cfg["train"]["metrics"])
print("Metrics implemented successfully")
# read settings from json file
# initlize optimizing methods : lr, scheduler of lr, optimizer
learning_rate = cfg["optimizer"]["lr"]
optimizer = get_optimizer(cfg)
optimizer = optimizer(model.parameters(), lr=learning_rate)
loss_fn = get_loss_fn(cfg)
criterion = loss_fn()
## Learning rate decay
max_lr = 3e-3 # Maximum LR
min_lr = cfg["optimizer"]["min_lr"] # Minimum LR
t_max = 10 # How many epochs to go from max_lr to min_lr
save_method = cfg["optimizer"]["lr_scheduler_factor"]
lr_patiences = cfg["optimizer"]["lr_patience"]
lr_factor = cfg["optimizer"]["reduce_lr_factor"]
scheduler = ReduceLROnPlateau(optimizer,
mode=save_method,
min_lr=min_lr,
patience=lr_patiences,
factor=lr_factor)
# scheduler = CosineAnnealingLR(optimizer, T_max=t_max, eta_min=min_lr)
print("\nTraing shape: {} samples".format(len(train_loader.dataset)))
print("Validation shape: {} samples".format(len(valid_loader.dataset)))
print("Beginning training...")
# export the result to log file
logging.info("--------------------------------")
logging.info("session name: {}".format(cfg["session"]["sess_name"]))
# logging.info(model)
logging.info("CONFIGS:")
logging.info(cfg)
# initialize the early_stopping object
checkpoint_path = os.path.join(log_dir, "Checkpoint.pt")
save_mode = cfg["train"]["mode"]
early_patience = cfg["train"]["early_patience"]
early_stopping = callbacks.EarlyStopping(patience=early_patience,
mode=save_mode,
path=checkpoint_path)
# training models
num_epoch = int(cfg["train"]["num_epoch"])
best_val_acc = 0
t0 = time.time()
for epoch in range(num_epoch):
t1 = time.time()
train_loss, train_acc, val_loss, val_acc, train_result, val_result = trainer.train_one_epoch(
epoch,
num_epoch,
model,
device,
train_loader,
valid_loader,
criterion,
optimizer,
train_metrics,
val_metrics,
)
train_checkpoint = {
'epoch': epoch + 1,
'valid_loss': val_loss,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
scheduler.step(val_loss)
## lr scheduling
logging.info(
"\n------Epoch %d / %d, Training time: %.4f seconds------" %
(epoch + 1, num_epoch, (time.time() - t1)))
logging.info("Training loss: {} - Other training metrics: {}".format(
train_loss, train_result))
logging.info(
"Validation loss: {} - Other validation metrics: {}".format(
val_loss, val_result))
## tensorboard
tb_writer.add_scalar("Training Loss", train_loss, epoch + 1)
tb_writer.add_scalar("Valid Loss", val_loss, epoch + 1)
tb_writer.add_scalar("Training Accuracy",
train_result["accuracy_score"], epoch + 1)
tb_writer.add_scalar("Valid Accuracy", val_result["accuracy_score"],
epoch + 1)
# tb_writer.add_scalar("training f1_score", train_result["f1_score"], epoch + 1)
# tb_writer.add_scalar("valid f1_score", val_result["f1_score"], epoch + 1)
# Save model
if save_mode == "min":
early_stopping(val_loss, train_checkpoint)
else:
early_stopping(val_acc, train_checkpoint)
if early_stopping.early_stop:
logging.info("Early Stopping!!!")
break
# testing on test set
# load the test model and making inference
print("\nInference on the testing set")
checkpoint = torch.load(checkpoint_path)
test_model = checkpoint['model']
test_model.load_state_dict(checkpoint['state_dict'])
test_model = test_model.to(device)
# logging report
report = tester.test_result(test_model, test_loader, device, cfg)
logging.info("\nClassification Report: \n {}".format(report))
logging.info('Completed in %.3f seconds.' % (time.time() - t0))
print("Classification Report: \n{}".format(report))
print('Completed in %.3f seconds.' % (time.time() - t0))
print(
'Start Tensorboard with tensorboard --logdir {}, view at http://localhost:6006/'
.format(log_dir))
def main(model,
dataset,
validation_flag,
comment="No comment",
checkpoint=None,
num_of_class=2):
# Checking cuda
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logging.info("Using device: {} ".format(device))
if checkpoint is not None:
print("...Load checkpoint from {}".format(checkpoint))
checkpoint = torch.load(checkpoint)
model.load_state_dict(checkpoint['state_dict'])
print("...Checkpoint loaded")
# Convert to suitable device
model = model.to(device)
print("Number of parameters: ", sum(p.numel() for p in model.parameters()))
logging.info("Model created...")
# using parsed configurations to create a dataset
data = cfg["data"]["data_csv_name"]
print("Reading training data from file: ", data)
training_set = pd.read_csv(data)
# check if validation flag is on
if validation_flag == 0:
# using custom validation set
print("Creating validation set from file")
valid = cfg["data"]["validation_csv_name"]
print("Reading validation data from file: ", valid)
valid_set = pd.read_csv(valid)
else:
# auto divide validation set
print("Splitting dataset into train and valid....")
validation_split = float(cfg["data"]["validation_ratio"])
training_set, valid_set, _, _ = data_split(training_set,
validation_split)
print("Done Splitting !!!")
data_path = cfg["data"]["data_path"]
batch_size = int(cfg["data"]["batch_size"])
# Create dataset
training_set = dataset(training_set, data_path, transform.train_transform)
valid_set = dataset(valid_set, data_path, transform.val_transform)
# End sampler
train_loader = torch.utils.data.DataLoader(training_set,
batch_size=batch_size,
shuffle=True)
val_loader = torch.utils.data.DataLoader(valid_set,
batch_size=batch_size,
shuffle=False)
logging.info("Dataset and Dataloaders created")
# create a metric for evaluating
# global train_metrics
# global val_metrics
train_metrics = metrics.Metrics(cfg["train"]["metrics"])
val_metrics = metrics.Metrics(cfg["train"]["metrics"])
print("Metrics implemented successfully")
# method to optimize the model
# read settings from json file
loss_function = cfg["optimizer"]["loss"]
optimizers = cfg["optimizer"]["name"]
learning_rate = cfg["optimizer"]["lr"]
# initlize optimizing methods : lr, scheduler of lr, optimizer
try:
# if the loss function comes from nn package
criterion = getattr(
nn, loss_function,
"The loss {} is not available".format(loss_function))
except:
# use custom loss
criterion = getattr(
custom_loss,
loss_function,
"The loss {} is not available".format(loss_function),
)
criterion = criterion()
optimizer = getattr(torch.optim, optimizers,
"The optimizer {} is not available".format(optimizers))
max_lr = 3e-3 # Maximum LR
min_lr = 1e-5 # Minimum LR
t_max = 10 # How many epochs to go from max_lr to min_lr
# optimizer = torch.optim.Adam(
# params=model.parameters(), lr=max_lr, amsgrad=False)
optimizer = optimizer(model.parameters(), lr=learning_rate)
save_method = cfg["train"]["lr_scheduler_factor"]
patiences = cfg["train"]["patience"]
lr_factor = cfg["train"]["reduce_lr_factor"]
scheduler = ReduceLROnPlateau(optimizer,
mode=save_method,
min_lr=min_lr,
patience=patiences,
factor=lr_factor)
# scheduler = CosineAnnealingLR(optimizer, T_max=t_max, eta_min=min_lr)
print("\nTraing shape: {} samples".format(len(train_loader.dataset)))
print("Validation shape: {} samples".format(len(val_loader.dataset)))
print("Beginning training...")
# export the result to log file
logging.info("--------------------------------")
logging.info("session name: {}".format(cfg["session"]["sess_name"]))
# logging.info(model)
logging.info("CONFIGS:")
logging.info(cfg)
# training models
num_epoch = int(cfg["train"]["num_epoch"])
best_val_acc = 0
t0 = time.time()
for epoch in range(0, num_epoch):
t1 = time.time()
print(('\n' + '%13s' * 3) % ('Epoch', 'gpu_mem', 'mean_loss'))
train_loss, val_loss, train_result, val_result = trainer.train_one_epoch(
epoch,
num_epoch,
model,
device,
train_loader,
val_loader,
criterion,
optimizer,
train_metrics,
val_metrics,
)
scheduler.step(val_loss)
# lr scheduling
logging.info(
"\n------Epoch %d / %d, Training time: %.4f seconds------" %
(epoch + 1, num_epoch, (time.time() - t1)))
logging.info("Training loss: {} - Other training metrics: {}".format(
train_loss, train_result))
logging.info(
"Validation loss: {} - Other validation metrics: {}".format(
val_loss, val_result))
tb_writer.add_scalar("Training Loss", train_loss, epoch + 1)
tb_writer.add_scalar("Valid Loss", val_loss, epoch + 1)
tb_writer.add_scalar("Training Accuracy",
train_result["accuracy_score"], epoch + 1)
tb_writer.add_scalar("Valid Accuracy", val_result["accuracy_score"],
epoch + 1)
# tb_writer.add_scalar("training f1_score", train_result["f1_score"], epoch + 1)
# tb_writer.add_scalar("valid f1_score", val_result["f1_score"], epoch + 1)
# saving epoch with best validation accuracy
if best_val_acc < float(val_result["accuracy_score"]):
logging.info("Validation accuracy= " +
str(val_result["accuracy_score"]))
logging.info("====> Save best at epoch {}".format(epoch + 1))
best_val_acc = val_result["accuracy_score"]
checkpoint = {
'epoch': epoch + 1,
'valid_loss': val_loss,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
torch.save(checkpoint, log_dir + "/Checkpoint.pt")
# testing on test set
test_data = cfg["data"]["test_csv_name"]
data_path = cfg["data"]["data_path"]
test_df = pd.read_csv(test_data)
# prepare the dataset
testing_set = dataset(test_df, data_path, transform.val_transform)
test_loader = torch.utils.data.DataLoader(testing_set,
batch_size=32,
shuffle=False)
print("\nInference on the testing set")
# load the test model and making inference
checkpoint = torch.load(log_dir + "/Checkpoint.pt")
test_model = checkpoint['model']
test_model.load_state_dict(checkpoint['state_dict'])
test_model = test_model.to(device)
# logging report
report = tester.test_result(test_model, test_loader, device, cfg)
logging.info("\nClassification Report: \n {}".format(report))
logging.info('%d epochs completed in %.3f seconds.' % (num_epoch,
(time.time() - t0)))
print("Classification Report: \n{}".format(report))
print('%d epochs completed in %.3f seconds.' % (num_epoch,
(time.time() - t0)))
print(
f'Start Tensorboard with "tensorboard --logdir {log_dir}", view at http://localhost:6006/'
)
def main():
parser = argparse.ArgumentParser(description='NA')
parser.add_argument('-c',
'--configure',
default='cfgs/chexphoto.cfg',
help='JSON file')
parser.add_argument('-cp',
'--checkpoint',
default=None,
help='checkpoint path')
args = parser.parse_args()
checkpoint = args.checkpoint
# read configure file
with open(args.configure) as f:
cfg = json.load(f)
time_str = str(datetime.now().strftime("%Y%m%d-%H%M"))
tensorboard_writer = logger.make_writer(cfg["session"]["sess_name"],
time_str)
# using parsed configurations to create a dataset
data = cfg["data"]["data_csv_name"]
valid = cfg['data']['test_csv_name']
data_path = cfg["data"]["data_path"]
batch_size = int(cfg["data"]["batch_size"])
validation_split = float(cfg["data"]["validation_ratio"])
# create dataset
training_set = pd.read_csv(data, usecols=["file_name", "label"])
valid_set = pd.read_csv(valid, usecols=["file_name", "label"])
# train, test, _, _ = dataloader.data_split(training_set, validation_split)
training_set = dataloader.ClassificationDataset(training_set, data_path,
transform.train_transform)
testing_set = dataloader.ClassificationDataset(valid_set, data_path,
transform.val_transform)
# create dataloaders
# global train_loader
# global val_loader
#SAmpler to prevent inbalance data label
# train_loader = torch.utils.data.DataLoader(training_set,sampler=ImbalancedDatasetSampler(training_set, callback_get_label=lambda x, i: tuple(x[i][1].tolist())),batch_size=batch_size,)
#End sampler
train_loader = torch.utils.data.DataLoader(
training_set,
batch_size=batch_size,
shuffle=True,
)
val_loader = torch.utils.data.DataLoader(
testing_set,
batch_size=batch_size,
shuffle=False,
)
# val_loader = torch.utils.data.DataLoader(testing_set,sampler=ImbalancedDatasetSampler(testing_set, callback_get_label=lambda x, i: tuple(x[i][1].tolist())),batch_size=batch_size,)
logging.info("Dataset and Dataloaders created")
# create a model
extractor_name = cfg["train"]["extractor"]
model = cls.ClassificationModel(model_name=extractor_name).create_model()
#load checkpoint to continue training
if checkpoint is not None:
print('...Load checkpoint from {}'.format(checkpoint))
checkpoint = torch.load(checkpoint)
model.load_state_dict(checkpoint)
print('...Checkpoint loaded')
classifier = nn.Sequential(nn.Linear(1408, 512, bias=True),
nn.ReLU(inplace=True),
nn.Linear(512, 6, bias=True))
# create classfier
# replace the last linear layer with your custom classifier
# model._avg_pooling = SPPLayer([1,2,4])
model._fc = classifier
# model.last_linear = self.cls
# select with layers to unfreeze
params = list(model.parameters())
len_param = len(params)
# for index,param in enumerate(model.parameters()):
# if index == (len_param -1):
# param.requires_grad = True
# else:
# param.requires_grad = False
# for param in model.parameters():
# print(param.requires_grad)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logging.info("Using device: {} ".format(device))
# convert to suitable device
# global model
model = model.to(device)
logging.info("Model created...")
# create a metric for evaluating
# global train_metrics
# global val_metrics
train_metrics = metrics.Metrics(cfg["train"]["metrics"])
val_metrics = metrics.Metrics(cfg["train"]["metrics"])
print("Metrics implemented successfully")
# method to optimize the model
# read settings from json file
loss_function = cfg["optimizer"]["loss"]
optimizers = cfg["optimizer"]["name"]
learning_rate = cfg["optimizer"]["lr"]
# initlize optimizing methods : lr, scheduler of lr, optimizer
try:
# if the loss function comes from nn package
criterion = getattr(
nn, loss_function,
"The loss {} is not available".format(loss_function))
except:
# use custom loss
criterion = getattr(
custom_loss,
loss_function,
"The loss {} is not available".format(loss_function),
)
criterion = criterion()
optimizer = getattr(torch.optim, optimizers,
"The optimizer {} is not available".format(optimizers))
max_lr = 3e-3 # Maximum LR
min_lr = 1e-5 # Minimum LR
t_max = 10 # How many epochs to go from max_lr to min_lr
# optimizer = torch.optim.Adam(
# params=model.parameters(), lr=max_lr, amsgrad=False)
optimizer = optimizer(model.parameters(), lr=learning_rate)
save_method = cfg["train"]["lr_scheduler_factor"]
patiences = cfg["train"]["patience"]
lr_factor = cfg["train"]["reduce_lr_factor"]
# scheduler = ReduceLROnPlateau(
# optimizer, save_method, patience=patiences, factor=lr_factor
# )
scheduler = CosineAnnealingLR(optimizer, T_max=t_max, eta_min=min_lr)
# before training, let's create a file for logging model result
log_file = logger.make_file(cfg["session"]["sess_name"], time_str)
logger.log_initilize(log_file)
print("Beginning training...")
# export the result to log file
f = open("saved/logs/traning_{}.txt".format(cfg["session"]["sess_name"]),
"a")
logging.info("-----")
logging.info("session name: {} \n".format(cfg["session"]["sess_name"]))
logging.info(model)
logging.info("\n")
logging.info("CONFIGS \n")
# logging the configs:
# logging.info(f.read())
# training models
num_epoch = int(cfg["train"]["num_epoch"])
best_val_acc = 0
for i in range(0, num_epoch):
loss, val_loss, train_result, val_result = trainer.train_one_epoch(
model,
train_loader,
val_loader,
device,
optimizer,
criterion,
train_metrics,
val_metrics,
)
# lr scheduling
logging.info(
"Epoch {} / {} \n Training loss: {} - Other training metrics: ".
format(i + 1, num_epoch, loss))
print("Epoch {} / {} \n Training acc: {} - Other training metrics: ".
format(i + 1, num_epoch, train_result["accuracy_score"]))
print("Epoch {} / {} \n Training loss: {} - Other training metrics: ".
format(i + 1, num_epoch, loss))
f.write(
"Epoch {} / {} \n Training loss: {} - Other training metrics: ".
format(i + 1, num_epoch, loss))
f.write("Epoch {} / {} \n Training acc: {} - Other training metrics: ".
format(i + 1, num_epoch, train_result["accuracy_score"]))
tensorboard_writer.add_scalar("training accuracy",
train_result["accuracy_score"], i + 1)
tensorboard_writer.add_scalar("training f1_score",
train_result["f1_score"], i + 1)
tensorboard_writer.add_scalar("training metrics", loss, i + 1)
logging.info(train_result)
logging.info(
" \n Validation loss : {} - Other validation metrics:".format(
val_loss))
print(
"Epoch {} / {} \n valid acc: {} - Other training metrics: ".format(
i + 1, num_epoch, val_result["accuracy_score"]))
f.write(" \n Validation loss : {} - Other validation metrics:".format(
val_loss))
tensorboard_writer.add_scalar("valid accuracy",
val_result["accuracy_score"], i + 1)
tensorboard_writer.add_scalar("valid f1_score", val_result["f1_score"],
i + 1)
tensorboard_writer.add_scalar("valid metrics", val_loss, i + 1)
logging.info(val_result)
logging.info("\n")
# saving epoch with best validation accuracy
if best_val_acc < float(val_result["accuracy_score"]):
logging.info("Validation accuracy= " +
str(val_result["accuracy_score"]) +
"===> Save best epoch")
f.write("Validation accuracy= " +
str(val_result["accuracy_score"]) + "===> Save best epoch")
best_val_acc = val_result["accuracy_score"]
torch.save(
model.state_dict(),
"saved/models/" + time_str + "-" +
cfg["train"]["save_as_name"],
)
scheduler.step(val_loss)
# else:
# # logging.info(
# # "Validation accuracy= "+ str(val_result["accuracy_score"])+ "===> No saving"
# # )
# continue
# testing on test set
test_data = cfg["data"]["test_csv_name"]
data_path = cfg["data"]["data_path"]
test_df = pd.read_csv(test_data, usecols=["file_name", "label"])
# prepare the dataset
testing_set = dataloader.ClassificationDataset(test_df, data_path,
transform.val_transform)
# make dataloader
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
test_loader = torch.utils.data.DataLoader(testing_set,
batch_size=32,
shuffle=False)
print("Inference on the testing set")
# load the test model and making inference
test_model = cls.ClassificationModel(
model_name=extractor_name).create_model()
model_path = os.path.join("saved/models",
time_str + "-" + cfg["train"]["save_as_name"])
test_model.load_state_dict(torch.load(model_path))
test_model = test_model.to(device)
logging.info(tester.test_result(test_model, test_loader, device, cfg))
def main():
# read configure file
with open("cfgs/tenes.cfg") as f:
cfg = json.load(f)
# using parsed configurations to create a dataset
data = cfg["data"]["data_csv_name"]
data_path = cfg["data"]["data_path"]
batch_size = int(cfg["data"]["batch_size"])
validation_split = float(cfg["data"]["validation_ratio"])
# create dataset
training_set = pd.read_csv(data, usecols=["image_name", "target"])
training_set["image_name"] = training_set["image_name"] + '.jpg'
training_set = shuffle(training_set)
training_set = training_set.sample(25000)
print(training_set['target'].value_counts())
train, test, _, _ = dataloader.data_split(training_set, validation_split)
training_set = dataloader.ClassificationDataset(train, data_path,
transform.train_transform)
testing_set = dataloader.ClassificationDataset(test, data_path,
transform.val_transform)
# create dataloaders
# global train_loader
# global val_loader
train_loader = torch.utils.data.DataLoader(
training_set,
batch_size=batch_size,
shuffle=True,
)
val_loader = torch.utils.data.DataLoader(
testing_set,
batch_size=batch_size,
shuffle=False,
)
logging.info("Dataset and Dataloaders created")
# create a model
extractor_name = cfg["train"]["extractor"]
model = cls.ClassificationModel(model_name=extractor_name).create_model()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logging.info("Using device: {} ".format(device))
# convert to suitable device
# global model
model = model.to(device)
logging.info("Model created...")
# create a metric for evaluating
# global train_metrics
# global val_metrics
train_metrics = metrics.Metrics(cfg["train"]["metrics"])
val_metrics = metrics.Metrics(cfg["train"]["metrics"])
print("Metrics implemented successfully")
# method to optimize the model
# read settings from json file
loss_function = cfg["optimizer"]["loss"]
optimizers = cfg["optimizer"]["name"]
learning_rate = cfg["optimizer"]["lr"]
# initlize optimizing methods : lr, scheduler of lr, optimizer
try:
# if the loss function comes from nn package
criterion = getattr(
nn, loss_function,
"The loss {} is not available".format(loss_function))
except:
# use custom loss
criterion = getattr(
custom_loss,
loss_function,
"The loss {} is not available".format(loss_function),
)
criterion = custom_loss.FocalLoss()
optimizer = getattr(torch.optim, optimizers,
"The optimizer {} is not available".format(optimizers))
optimizer = optimizer(model.parameters(), lr=learning_rate)
save_method = cfg["train"]["lr_scheduler_factor"]
patiences = cfg["train"]["patience"]
lr_factor = cfg["train"]["reduce_lr_factor"]
scheduler = ReduceLROnPlateau(optimizer,
save_method,
patience=patiences,
factor=lr_factor)
# before training, let's create a file for logging model result
time_str = str(datetime.now().strftime("%Y%m%d-%H%M"))
log_file = logger.make_file(cfg["session"]["sess_name"], time_str)
logger.log_initilize(log_file)
print("Beginning training...")
# export the result to log file
logging.info("-----")
logging.info("session name: {} \n".format(cfg["session"]["sess_name"]))
logging.info("Training size: " + str(len(train)))
logging.info("Validation size: " + str(len(test)))
logging.info(model)
logging.info("\n")
logging.info("CONFIGS \n")
# logging the configs:
# training models
num_epoch = int(cfg["train"]["num_epoch"])
best_val_acc = 0
for i in range(0, num_epoch):
loss, val_loss, train_result, val_result = trainer.train_one_epoch(
model,
train_loader,
val_loader,
device,
optimizer,
criterion,
train_metrics,
val_metrics,
)
# lr scheduling
scheduler.step(val_loss)
logging.info(
"Epoch {} / {} \n Training loss: {} - Other training metrics: ".
format(i + 1, num_epoch, loss))
logging.info(train_result)
logging.info(
" \n Validation loss : {} - Other validation metrics:".format(
val_loss))
logging.info(val_result)
logging.info("\n")
# saving epoch with best validation accuracy
if best_val_acc < float(val_result["f1_score"]):
logging.info("Validation f1= " + str(val_result["f1_score"]) +
"===> Save best epoch")
best_val_acc = val_result["f1_score"]
torch.save(
model.state_dict(),
"saved/models/" + time_str + "-" +
cfg["train"]["save_as_name"],
)
else:
logging.info("Validation f1= " + str(val_result["f1_score"]) +
"===> No saving")
continue
# testing on test set
test_data = cfg["data"]["test_csv_name"]
data_path = cfg["data"]["data_path"]
test_df = pd.read_csv(test_data, usecols=["image_name", "target"])
test_df['image_name'] = test_df['image_name'] + '.jpg'
# prepare the dataset
testing_set = dataloader.TestDataset(test_df, 'dataset/test/test',
transform.test_transform)
# make dataloader
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
test_loader = torch.utils.data.DataLoader(testing_set,
batch_size=16,
shuffle=False)
print("\n Inference on the testing set")
# load the test model and making inference
test_model = cls.ClassificationModel(
model_name=extractor_name).create_model()
model_path = os.path.join("saved/models",
time_str + "-" + cfg["train"]["save_as_name"])
test_model.load_state_dict(torch.load(model_path))
test_model = test_model.to(device)
logging.info(tester.test_result(test_model, test_loader, device))
def main(collocation,
model,
dataset,
validation_flag,
current_fold,
comment="No comment",
checkpoint=None,
logger=None,
num_of_class=2):
# read training set
data = cfg["data"]["data_csv_name"]
data = re.sub(r"fold[0-9]", str(current_fold), data)
print("Reading training data from file: ", data)
training_set = pd.read_csv(data, delimiter="*", header=None)
# check if validation flag is on
if validation_flag == 1:
# using custom validation set
print("Creating validation set from file")
valid = cfg["data"]["validation_csv_name"]
valid = re.sub(r"fold[0-9]", str(current_fold), valid)
print("Reading validation data from file: ", valid)
valid_set = pd.read_csv(valid, delimiter="*", header=None)
else:
# auto divide validation set
validation_split = float(cfg["data"]["validation_ratio"])
training_set, valid_set = data_split(training_set, validation_split)
data_path = cfg["data"]["data_path"]
batch_size = int(cfg["data"]["batch_size"])
# create dataset
training_set = dataset(training_set,
data_path,
padding=True,
normalize=True)
testing_set = dataset(valid_set, data_path, padding=True, normalize=True)
# End sampler
train_loader = torch.utils.data.DataLoader(training_set,
batch_size=batch_size,
shuffle=True,
collate_fn=collocation)
val_loader = torch.utils.data.DataLoader(testing_set,
batch_size=batch_size,
shuffle=False,
collate_fn=collocation)
# val_loader = torch.utils.data.DataLoader(testing_set,sampler=ImbalancedDatasetSampler(testing_set, callback_get_label=lambda x, i: tuple(x[i][1].tolist())),batch_size=batch_size,)
logging.info("Dataset and Dataloaders created")
# create a model
# extractor_name = cfg["train"]["extractor"]
# model = cls(model_name=extractor_name).create_model()
# model = cls(
# num_blocks=6,
# in_channels=1,
# out_channels=64,
# bottleneck_channels=0,
# kernel_sizes=8,
# num_pred_classes=2
# )
model = cls(class_num=2,
num_of_blocks=9,
training=True,
dense_layers=[256, 256])
for param in model.parameters():
param.requires_grad = True
# load checkpoint to continue training
if checkpoint is not None:
print("...Load checkpoint from {}".format(checkpoint))
checkpoint = torch.load(checkpoint)
model.load_state_dict(checkpoint)
print("...Checkpoint loaded")
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logging.info("Using device: {} ".format(device))
# convert to suitable device
# global model
model = model.to(device)
print(sum(p.numel() for p in model.parameters()))
time.sleep(4)
logging.info("Model created...")
# create a metric for evaluating
# global train_metrics
# global val_metrics
train_metrics = metrics.Metrics(cfg["train"]["metrics"])
val_metrics = metrics.Metrics(cfg["train"]["metrics"])
print("Metrics implemented successfully")
# method to optimize the model
# read settings from json file
loss_function = cfg["optimizer"]["loss"]
optimizers = cfg["optimizer"]["name"]
learning_rate = cfg["optimizer"]["lr"]
# initlize optimizing methods : lr, scheduler of lr, optimizer
try:
# if the loss function comes from nn package
criterion = getattr(
nn, loss_function,
"The loss {} is not available".format(loss_function))
except:
# use custom loss
criterion = getattr(
custom_loss,
loss_function,
"The loss {} is not available".format(loss_function),
)
criterion = custom_loss.WeightedFocalLoss(weight=None,
gamma=2,
reduction="sum")
# criterion = nn.CrossEntropyLoss(reduction='none')
optimizer = getattr(torch.optim, optimizers,
"The optimizer {} is not available".format(optimizers))
max_lr = 3e-3 # Maximum LR
min_lr = 1e-5 # Minimum LR
t_max = 10 # How many epochs to go from max_lr to min_lr
optimizer = optimizer(model.parameters(), lr=learning_rate, momentum=0.9)
save_method = cfg["train"]["lr_scheduler_factor"]
patiences = cfg["train"]["patience"]
lr_factor = cfg["train"]["reduce_lr_factor"]
# scheduler = ReduceLROnPlateau(
# optimizer, save_method, patience=patiences, factor=lr_factor
# )
scheduler = ReduceLROnPlateau(
optimizer,
mode=save_method,
factor=lr_factor,
min_lr=0.00001,
verbose=True,
patience=patiences,
)
# before training, let's create a neptune protocol for tracking experiment
neptune.init("deepbox/gtopia-ml")
PARAMS = {
"loss_function": cfg["optimizer"]["loss"],
"optimizers": cfg["optimizer"]["name"],
"learning_rate": cfg["optimizer"]["lr"],
"lr_factor": cfg["train"]["reduce_lr_factor"],
"patiences": cfg["train"]["patience"],
"loss_function": cfg["optimizer"]["loss"],
"data_path": cfg["data"]["data_csv_name"],
"batch_size": batch_size,
}
# create neptune experiment
neptune.create_experiment(
name=comment + "_" + str(current_fold),
params=PARAMS,
tags=[
str(current_fold), cfg["train"]["model.class"], cfg["data"]["mode"]
],
)
logging.info("Created experiment tracking protocol")
print("Beginning training...")
print("Traing shape: ", len(train_loader.dataset))
print("Validation shape: ", len(val_loader.dataset))
time.sleep(3)
# export the result to log file
logging.info("-----")
logging.info("session name: {} \n".format(cfg["session"]["sess_name"]))
logging.info("session description: {} \n".format(comment))
logging.info(model)
logging.info("\n")
logging.info("CONFIGS \n")
# training models
num_epoch = int(cfg["train"]["num_epoch"])
best_val_acc = 0
for i in range(0, num_epoch):
loss, val_loss, train_result, val_result = trainer.train_one_epoch(
model,
train_loader,
val_loader,
device,
optimizer,
criterion,
train_metrics,
val_metrics,
num_of_class,
)
# neptune logging
neptune.log_metric("train_loss", loss)
neptune.log_metric("validation_loss", val_loss)
for single_metric in train_result.keys():
neptune.log_metric("train_" + single_metric,
train_result[single_metric])
neptune.log_metric("val_" + single_metric,
val_result[single_metric])
# lr scheduling
logging.info(
"Epoch {} / {} \n Training loss: {} - Other training metrics: ".
format(i + 1, num_epoch, loss))
logging.info(train_result)
logging.info(
" \n Validation loss : {} - Other validation metrics:".format(
val_loss))
logging.info(val_result)
logging.info("\n")
# saving epoch with best validation accuracy
if best_val_acc < float(val_result["f1_score"]):
logging.info("Validation f1= " + str(val_result["f1_score"]) +
"===> Save best epoch \n")
best_val_acc = val_result["f1_score"]
torch.save(
model.state_dict(),
"saved/models/" + time_str + "-" + str(current_fold) + "-" +
cfg["train"]["save_as_name"],
)
scheduler.step(val_loss)
# testing on test set
test_data = cfg["data"]["test_csv_name"]
data_path = cfg["data"]["data_path"]
test_data = re.sub(r"fold[0-9]", str(current_fold), test_data)
print("reading testing data from file: ", test_data)
test_df = pd.read_csv(test_data, delimiter="*", header=None)
# prepare the dataset
testing_set = dataset(test_df, data_path, padding=False, normalize=True)
# make dataloader
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
test_loader = torch.utils.data.DataLoader(testing_set,
batch_size=1,
shuffle=False,
collate_fn=collocation)
print("Inference on the testing set")
# load the test model and making inference
test_model = cls(class_num=2,
num_of_blocks=9,
training=True,
dense_layers=[256, 256])
# test_model = cls(
# num_blocks=6,
# in_channels=1,
# out_channels=64,
# bottleneck_channels=0,
# kernel_sizes=8,
# num_pred_classes=2,
# )
model_path = os.path.join(
"saved/models",
time_str + "-" + str(current_fold) + "-" +
cfg["train"]["save_as_name"],
)
test_model.load_state_dict(torch.load(model_path))
test_model = test_model.to(device)
logging.info(
tester.adaptive_test_result(test_model, test_loader, device, cfg,
num_of_class))
f = open("test_report.txt", "w")
f.write("Test results \n : {}".format(
tester.adaptive_test_result(test_model, test_loader, device, cfg)))
f.close()
# send some versions of code
neptune.log_artifact("test_report.txt")
neptune.log_artifact("data_loader/dataloader.py")
neptune.log_artifact("cfgs/tenes.cfg")
neptune.log_artifact("trainer.py")
neptune.log_artifact("test.py")
neptune.log_artifact("run_exp_2.py")
if (cfg["train"]["model.class"] == "Lecnet"):
neptune.log_artifact("model/classification.py")
else:
neptune.log_artifact("model/benchmark.py")
# saving torch models
print("---End of testing phase----")
neptune.stop()
def main(collocation,model,dataset,validation_flag,current_fold,comment="No comment", checkpoint=None,logger=None):
# parser = argparse.ArgumentParser(description='NA')
# parser.add_argument('-c', '--configure', default='cfgs/chexphoto.cfg', help='JSON file')
# parser.add_argument('-cp', '--checkpoint', default=None, help = 'checkpoint path')
# args = parser.parse_args()
# checkpoint = args.checkpoint
# # read configure file
# with open(args.configure) as f:
# cfg = json.load(f)
# using parsed configurations to create a dataset
# read training set
data = cfg["data"]["data_csv_name"]
data = re.sub(r"fold[0-9]",str(current_fold),data)
print("Reading training data from file: ",data)
training_set = pd.read_csv(data,delimiter='*',header=None)
# check if validation flag is on
if (validation_flag==1):
# using custom validation set
print("Creating validation set from file")
valid = cfg["data"]["validation_csv_name"]
valid = re.sub(r"fold[0-9]",str(current_fold),valid)
print("Reading validation data from file: ",valid)
valid_set = pd.read_csv(valid,delimiter='*',header=None)
else:
# auto divide validation set
validation_split = float(cfg["data"]["validation_ratio"])
training_set,valid_set = data_split(training_set,validation_split)
data_path = cfg["data"]["data_path"]
batch_size = int(cfg["data"]["batch_size"])
# create dataset
# train, test, _, _ = dataloader.data_split(training_set, validation_split)
training_set = dataset(
training_set, data_path, padding=True,normalize=True
)
testing_set = dataset(
valid_set, data_path, padding=True,normalize=True
)
# create dataloaders
# global train_loader
# global val_loader
# SAmpler to prevent inbalance data label
# train_loader = torch.utils.data.DataLoader(training_set,sampler=ImbalancedDatasetSampler(training_set, callback_get_label=lambda x, i: tuple(x[i][1].tolist())),batch_size=batch_size,)
# End sampler
train_loader = torch.utils.data.DataLoader(
training_set, batch_size=batch_size, shuffle=True,collate_fn=collocation
)
val_loader = torch.utils.data.DataLoader(
testing_set, batch_size=batch_size, shuffle=False,collate_fn=collocation
)
# val_loader = torch.utils.data.DataLoader(testing_set,sampler=ImbalancedDatasetSampler(testing_set, callback_get_label=lambda x, i: tuple(x[i][1].tolist())),batch_size=batch_size,)
logging.info("Dataset and Dataloaders created")
# create a model
# extractor_name = cfg["train"]["extractor"]
# model = cls(model_name=extractor_name).create_model()
model = cls(class_num=2,num_of_blocks=9,training=True,dense_layers=[256,256])
# model = cls( num_blocks = 8, in_channels=1,out_channels=64,bottleneck_channels=0,kernel_sizes=8,num_pred_classes=2)
# load checkpoint to continue training
if checkpoint is not None:
print("...Load checkpoint from {}".format(checkpoint))
checkpoint = torch.load(checkpoint)
model.load_state_dict(checkpoint)
print("...Checkpoint loaded")
classifier = nn.Sequential(
nn.Linear(1408, 512, bias=True),
nn.ReLU(inplace=True),
nn.Linear(512, 6, bias=True),
)
# create classfier
# replace the last linear layer with your custom classifier
# model._avg_pooling = SPPLayer([1,2,4])
# model._fc = classifier
# model.last_linear = self.cls
# select with layers to unfreeze
params = list(model.parameters())
len_param = len(params)
# for index,param in enumerate(model.parameters()):
# if index == (len_param -1):
# param.requires_grad = True
# else:
# param.requires_grad = False
# for param in model.parameters():
# print(param.requires_grad)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logging.info("Using device: {} ".format(device))
# convert to suitable device
# global model
model = model.to(device)
print(sum(p.numel() for p in model.parameters()))
time.sleep(4)
logging.info("Model created...")
# create a metric for evaluating
# global train_metrics
# global val_metrics
train_metrics = metrics.Metrics(cfg["train"]["metrics"])
val_metrics = metrics.Metrics(cfg["train"]["metrics"])
print("Metrics implemented successfully")
# method to optimize the model
# read settings from json file
loss_function = cfg["optimizer"]["loss"]
optimizers = cfg["optimizer"]["name"]
learning_rate = cfg["optimizer"]["lr"]
# initlize optimizing methods : lr, scheduler of lr, optimizer
try:
# if the loss function comes from nn package
criterion = getattr(
nn, loss_function, "The loss {} is not available".format(loss_function)
)
except:
# use custom loss
criterion = getattr(
custom_loss,
loss_function,
"The loss {} is not available".format(loss_function),
)
criterion = custom_loss.WeightedFocalLoss(weight=None, gamma=2,reduction='mean')
# criterion = nn.CrossEntropyLoss(reduction='none')
optimizer = getattr(
torch.optim, optimizers, "The optimizer {} is not available".format(optimizers)
)
max_lr = 3e-3 # Maximum LR
min_lr = 1e-5 # Minimum LR
t_max = 10 # How many epochs to go from max_lr to min_lr
# optimizer = torch.optim.Adam(
# params=model.parameters(), lr=max_lr, amsgrad=False)
optimizer = optimizer(model.parameters(), lr=learning_rate)
save_method = cfg["train"]["lr_scheduler_factor"]
patiences = cfg["train"]["patience"]
lr_factor = cfg["train"]["reduce_lr_factor"]
# scheduler = ReduceLROnPlateau(
# optimizer, save_method, patience=patiences, factor=lr_factor
# )
scheduler = ReduceLROnPlateau(optimizer, mode='min',factor=0.5,min_lr=0.00001,verbose=True,patience=5)
# before training, let's create a file for logging model result
print("Beginning training...")
time.sleep(3)
# export the result to log file
logging.info("-----")
logging.info("session name: {} \n".format(cfg["session"]["sess_name"]))
logging.info("session description: {} \n".format(comment))
logging.info(model)
logging.info("\n")
logging.info("CONFIGS \n")
# logging the configs:
# logging.info(f.read())
# training models
num_epoch = int(cfg["train"]["num_epoch"])
best_val_acc = 0
for i in range(0, num_epoch):
loss, val_loss, train_result, val_result = trainer.train_one_epoch(
model,
train_loader,
val_loader,
device,
optimizer,
criterion,
train_metrics,
val_metrics,
)
# lr scheduling
logging.info(
"Epoch {} / {} \n Training loss: {} - Other training metrics: ".format(
i + 1, num_epoch, loss
)
)
# tensorboard_writer.add_scalar("training accuracy",train_result["accuracy_score"],i + 1)
# tensorboard_writer.add_scalar("training f1_score",train_result["f1_score"],i + 1)
# tensorboard_writer.add_scalar("training metrics",loss,i + 1)
logging.info(train_result)
logging.info(
" \n Validation loss : {} - Other validation metrics:".format(val_loss)
)
# tensorboard_writer.add_scalar("valid accuracy",val_result["accuracy_score"],i + 1)
# tensorboard_writer.add_scalar("valid f1_score",val_result["f1_score"],i + 1)
# tensorboard_writer.add_scalar("valid metrics",val_loss,i + 1)
logging.info(val_result)
logging.info("\n")
# saving epoch with best validation accuracy
if best_val_acc < float(val_result["f1_score"]):
logging.info(
"Validation f1= "
+ str(val_result["f1_score"])
+ "===> Save best epoch \n"
)
best_val_acc = val_result["f1_score"]
torch.save(
model.state_dict(),
"saved/models/" + time_str + "-" + str(current_fold) + "-" +cfg["train"]["save_as_name"],
)
scheduler.step(val_loss)
# else:
# # logging.info(
# # "Validation accuracy= "+ str(val_result["accuracy_score"])+ "===> No saving"
# # )
# continue
# testing on test set
test_data = cfg["data"]["test_csv_name"]
data_path = cfg["data"]["data_path"]
test_data = re.sub(r"fold[0-9]",str(current_fold),test_data)
print("reading testing data from file: ",test_data)
test_df = pd.read_csv(test_data,delimiter='*',header=None)
# prepare the dataset
testing_set = dataset(
test_df, data_path, padding=False, normalize=True
)
# make dataloader
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
test_loader = torch.utils.data.DataLoader(testing_set, batch_size=1, shuffle=False,collate_fn=collocation)
print("Inference on the testing set")
# load the test model and making inference
test_model = cls(class_num=2,num_of_blocks=9,training=True,dense_layers=[256,256])
# test_model = cls( num_blocks = 8, in_channels=1,out_channels=64,bottleneck_channels=0,kernel_sizes=8,num_pred_classes=2)
model_path = os.path.join(
"saved/models", time_str + "-" + str(current_fold) + "-" + cfg["train"]["save_as_name"]
)
test_model.load_state_dict(torch.load(model_path))
test_model = test_model.to(device)
logging.info(tester.adaptive_test_result(test_model, test_loader, device, cfg))
# saving torch models
print("---End of testing phase----")
