def main():
print_config()
dataset = load_dataset(CONFIG['dataset'], CONFIG['dataset_path'])
env = TextLocEnv(dataset.image_paths, dataset.bounding_boxes)
m = CustomModel(10)
vs = [m(env.reset())]
g = c.build_computational_graph(vs)
with open('graph.dot', 'w') as o:
o.write(g.dump())
def main():
print_config()
relative_paths = np.loadtxt(CONFIG['imagefile_path'], dtype=str)
images_base_path = os.path.dirname(CONFIG['imagefile_path'])
absolute_paths = [images_base_path + i.strip('.') for i in relative_paths]
bboxes = np.load(CONFIG['boxfile_path'], allow_pickle=True)
env = TextLocEnv(absolute_paths, bboxes, -1)
m = CustomModel(10)
vs = [m(env.reset())]
g = c.build_computational_graph(vs)
with open('graph.dot', 'w') as o:
o.write(g.dump())
def main():
print_config()
relative_paths = np.loadtxt(CONFIG['imagefile_path'], dtype=str)
images_base_path = os.path.dirname(CONFIG['imagefile_path'])
absolute_paths = [images_base_path + i.strip('.') for i in relative_paths]
bboxes = np.load(CONFIG['boxfile_path'], allow_pickle=True)
env = TextLocEnv(absolute_paths, bboxes, -1)
q_func = chainerrl.q_functions.SingleModelStateQFunctionWithDiscreteAction(
CustomModel(9))
optimizer = chainer.optimizers.Adam(eps=1e-2)
optimizer.setup(q_func)
replay_buffer = chainerrl.replay_buffer.ReplayBuffer(
capacity=CONFIG['replay_buffer_capacity'])
explorer = chainerrl.explorers.ConstantEpsilonGreedy(
epsilon=0, random_action_func=env.action_space.sample)
agent = chainerrl.agents.DQN(
q_func,
optimizer,
replay_buffer,
CONFIG['gamma'],
explorer,
gpu=CONFIG['gpu_id'],
replay_start_size=CONFIG['replay_start_size'],
update_interval=CONFIG['update_interval'],
target_update_interval=CONFIG['target_update_interval'])
agent.load(CONFIG['resultdir_path'] + '/best')
actions = defaultdict(int)
with open('iou.txt', 'w') as f:
i = 0
for j in range(100):
obs = env.reset()
done = False
while (not done) and i < 100:
#print(i,j)
action = agent.act(obs)
actions[ACTION_MEANINGS[action]] += 1
obs, reward, done, info = env.step(action)
print(ACTION_MEANINGS[action], reward, done, info)
if done:
f.write(f'{env.iou}\n')
#input()
i += 1