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))
# read configure file
with open(args.configure) as f:
cfg = json.load(f)
# comment for this experiment: leave here
comment = cfg["session"]["sess_name"]
# create dir to save log and checkpoint
save_path = cfg['train']['save_path']
time_str = str(datetime.now().strftime("%Y%m%d-%H%M"))
sess_name = cfg["session"]["sess_name"]
log_dir = make_dir_epoch_time(save_path, sess_name, time_str)
# create logger
log_file = logger.make_file(log_dir, 'result.txt')
logger.log_initilize(log_file)
tb_writer = logger.make_writer(log_dir=log_dir)
logging.info(
f"Start Tensorboard with tensorboard --logdir {log_dir}, view at http://localhost:6006/"
)
print("--------All checkpoint will be saved to ```{}```--------".format(
log_dir))
cls_model = model_loader(cfg)
print("Successfully imported model module")
print("Done Loading!!!\n")
time.sleep(2.7)
main(
config=cfg,
model=cls_model,
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))