np.random.seed(2)
BATCH_SIZE = 128
def list_split(l, size):
return [l[m:m + size] for m in range(0, len(l), size)]
if __name__ == '__main__':
images_dir = '/home/hsli/gnode02/imagenet-data/train/'
feature_extractor = load_model(
'checkpoints/mobilenetv2_predictor_2W_acc_0.6955_epoch50.hdf5')
rm = ResultManager('results')
agent_acc_size_dict = []
origin_acc_size_dict = []
agent = Q_Agent(s_dim=2,
a_dim=10,
epsilon_decay=0.9993,
epsilon_min=0.2,
lr=0.1,
gamma=0.95)
step_count = 0
env = BatchImgEnvironment(imagenet_train_path=images_dir,
samples_per_class=3,
backbone_model=InceptionV3(),
if len(self.agent_memory['reward']) < self.recent_zone:
recent_reward = np.mean(self.agent_memory['reward'])
recent_acc = np.mean(self.agent_memory['accuracy'])
else:
recent_reward = np.mean(
self.agent_memory['reward'][-self.recent_zone:])
recent_acc = np.mean(
self.agent_memory['accuracy'][-self.recent_zone:])
return recent_acc, recent_reward
#
if __name__ == '__main__':
api = Baidu()
rm = ResultManager('evaluation_results')
running_agent = RunningAgent(
dqn_path='evaluation_results/agent_DQN_train_baidu_imagenet.h5',
banchmark_q=75,
cloud_backend=api,
)
imagenet_paths = _gen_sample_set_imagenet(
'/home/hsli/gnode02/imagenet-data/train/', 3)[-500:]
test_image_paths = imagenet_paths
for idx, path in enumerate(test_image_paths):
error_code, log_dict = running_agent.agent_upload(path)
if error_code > 0: continue
def estimate(self):
if len(self.agent_memory['reward']) < self.recent_zone:
recent_reward = np.mean(self.agent_memory['reward'])
recent_acc = np.mean(self.agent_memory['accuracy'])
else:
recent_reward = np.mean(self.agent_memory['reward'][-self.recent_zone:])
recent_acc = np.mean(self.agent_memory['accuracy'][-self.recent_zone:])
return recent_acc, recent_reward
#
if __name__ == '__main__':
api = Baidu()
# api = FacePP()
rm = ResultManager('result')
running_agent = RunningAgent(# dqn_path='evaluation_results/agent_DQN_train_baidu_DNIM.h5',
# dqn_path='evaluation_results/agent_DQN_train_face_imagenet.h5',
# dqn_path='evaluation_results/agent_DQN_train_face_DNIM.h5',
# dqn_path='evaluation_results/agent_DQN_retrain_face_DNIM.h5',
# dqn_path='evaluation_results/agent_DQN_retrain_baidu_DNIM.h5',
dqn_path='result/agent_DQN_train_baidu_imagenet.h5',
banchmark_q=75,
cloud_backend=api,
)
# imagenet_paths = _gen_sample_set_imagenet('/home/hsli/gnode02/imagenet-data/train/', 2)
# imagenet_paths = _gen_sample_set_imagenet('/home/imagenet-data/train/', 2)
with open("result/imagenet_baidu_ref2000.pkl", "rb") as file: