feature_extractor = Model(inputs=feature_extractor.input, outputs=x) rm = ResultManager('evaluation_results') agent_acc_size_dict = [] origin_acc_size_dict = [] agent = DQN_Agent(s_dim=1280, a_dim=10, epsilon_decay=0.99, epsilon_min=0.02, gamma=0.95, replay_batchsize=256) if EVALUATION: agent.model = load_model( 'evaluation_results/agent_DQN_train_amazon_imagenet.h5' ) # If in evaluation phase, replace this with the actual pretrained model agent.curr_exploration_rate = 0 step_count = 0 env = EnvironmentAPI( imagenet_train_path=images_dir, # cloud_agent=AmazonRekognition(), cloud_agent=FacePP(), dataset='imagenet', # cache_path='evaluation_results/image_reference_cache_amazon.defaultdict') cache_path='evaluation_results/image_reference_cache_face.defaultdict') # In order to reduce some billing recognition service requests, I cached the recognized result locally. # Can be replaced by loading an empty dict from a pickled file. Navigate to the code for more details.
test_image_paths = imagenet_paths[:10] print(test_image_paths) flag = 1 for index, path in enumerate(test_image_paths): image = Image.open(path).convert("RGB") image_data = preprocess_input( np.expand_dims(np.asarray(image.resize((224, 224)), dtype=np.float32), axis=0)) # features = feature_extractor.predict(image_data)[0][0][0] # state_actions, action_id = agent.choose_action(features) # action = [i for i in np.arange(5, 105, 10)][action_id] print(index) print(array) # time.sleep(1) if flag == 1: print("xxx") print(array) handle_process.join() agent.model = array[0] flag = 0 # if __name__ == "__main__": # main() # array = multiprocessing.Manager().list() # print('父进程pid: %d' % os.getpid()) # os.getpid获取当前进程的进程号 # handle_process = Process(target=process_handle, args=(array,)) # handle_process.start() # # handle_process.join() # array.extend([5, 6, 7]) # print(array)
# test_image_paths = FLIR[:1000] cloud_agent = Baidu() train_log = defaultdict(list) ref_results = defaultdict(dict) compress_results = defaultdict(dict) choose_action_total_time = 0 feedback_total_time = 0 feature_total_time = 0 recent_accuracy = 0 recent_reward = 0 start_time = time.time() if EVALUATION: agent.model = load_model('compute_time_results/baidu_imagenet.h5') # agent.model = load_model('compute_time_results/baidu_FLIR.h5') agent.curr_exploration_rate = 0 load_model_time = time.time() # init feature compute_memory(103) image = Image.open(test_image_paths[0]).convert("RGB") image_data = preprocess_input( np.expand_dims(np.asarray(image.resize((224, 224)), dtype=np.float32), axis=0)) features = feature_extractor.predict(image_data)[0][0][0] compute_memory(104) # for for step_count, path in enumerate(test_image_paths): compute_memory(step_count) image = Image.open(path).convert("RGB")