"GPU": 1
},
inter_op_parallelism_threads=10)
with Sess(options, meta, config=config) as sess:
workers = []
for i in range(settings.NB_ACTORS):
with tf.device("/device:CPU:" + str(i)):
workers.append(Actor.Actor(sess, i + 1))
print("Initializing learner...")
with tf.device("/device:GPU:0"):
learner = Learner(sess, *workers[0].get_env_features())
print("Learner initialized !\n")
if settings.LOAD:
learner.load()
threads = []
for i in range(settings.NB_ACTORS):
thread = threading.Thread(target=workers[i].run)
threads.append(thread)
threads.append(threading.Thread(target=learner.run))
if settings.GUI:
GUI_thread = threading.Thread(target=GUI.main)
GUI_thread.start()
sess.run(tf.global_variables_initializer())
for t in threads:
thr_conf = 0.8
alpha = 0.2
if __name__ == '__main__':
seed_everything(config.seed)
model = Unet(encoder_name='resnet50', encoder_weights='imagenet', decoder_use_batchnorm=True,
decoder_attention_type='scse', classes=2, activation=None)
df = pd.read_csv(config.df_path)
train_df = df[df.fold != config.fold].reset_index(drop=True)
valid_df = df[df.fold == config.fold].reset_index(drop=True)
transforms = get_transforms(config.input_size, need=('train', 'val'))
train_dataset = dsbDataset(config.data_dir, config.scr_dir, config.mask_dir, train_df,
tfms=transforms['train'], return_id=False)
valid_dataset = dsbDataset(config.data_dir, config.scr_dir, config.mask_dir, valid_df,
tfms=transforms['val'], return_id=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=config.batch_size, num_workers=config.num_workers,
shuffle=True)
valid_loader = DataLoader(dataset=valid_dataset, batch_size=1, num_workers=config.num_workers,
shuffle=False)
Learner = Learner(model, train_loader, valid_loader, config)
pretrained_path = os.path.join(config.log_dir, 'best_model.pth')
if os.path.isfile(pretrained_path):
Learner.load(pretrained_path)
Learner.log(f"Checkpoint Loaded: {pretrained_path}")
Learner.fit(config.n_epochs)
