Question: I am referring to this repo to adapt mmaction2 grad-cam demo from short video offline inference to long video online inference. The script is shown below: Note: to make this script can be easily reproduce, i comment out some codes that needs many dependencies. import cv2 import numpy as np import torchvision.transforms as transforms import sys from PIL import Image #from mmaction.apis import init_recognizer #from utils.gradcam_utils import GradCAM import torch import asyncio from concurrent.futures import ProcessPoolExecutor from functools import partial # sys.path.append('./utils') async def preprocess_img(arr): image = Image.fromarray(np.uint8(arr)) mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] transform = transforms.Compose([ transforms.Resize((model_input_height, model_input_width)), transforms.ToTensor(), transforms.Normalize(mean, std, inplace=False), ]) normalized_img = transform(image) img_np = normalized_img.numpy() return img_np async def inference(frame_buffer): print("starting inference") # inputs = {} # input_tensor = torch.from_numpy(frame_buffer).type(torch.FloatTensor) # input_cuda_tensor = input_tensor.cuda() # inputs['imgs'] = input_cuda_tensor # results = gradcam(inputs) # display_buffer = np.squeeze(results[0].cpu().detach().numpy(), axis=0) # return display_buffer async def run_blocking_func(loop_, queue_, frame_buffer): with ProcessPoolExecutor() as pool: blocking_func = partial(inference, frame_buffer) frame = await loop_.run_in_executor(pool, blocking_func) print(frame) await queue_.put(frame) await asyncio.sleep(0.01) async def get_frames(capture): capture.grab() ret, frame = capture.retrieve() if not ret: print("empty frame") return for i in range(32): img = await preprocess_img(frame) expandimg = np.expand_dims(img, axis=(0, 1, 3)) print(f'expandimg.shape{expandimg.shape}') frame_buffer[:, :, :, i, :, :] = expandimg[:, :, :, 0, :, :] return frame_buffer async def show_frame(queue_: asyncio.LifoQueue): display_buffer = await queue_.get() for i in range(32): blended_image = display_buffer[i, :, :, :] cv2.imshow('Grad-CAM VIS', blended_image) if cv2.waitKey(10) & 0xFF == ord('q'): cap.release() cv2.destroyAllWindows() break async def produce(loop_, queue_, cap): while True: frame_buffer = await asyncio.create_task(get_frames(cap)) # Apply Grad-CAM display_buffer = await asyncio.create_task(run_blocking_func(loop_, queue_,frame_buffer)) await queue_.put(display_buffer) async def consume(queue_): while True: if queue_.qsize(): task1 = asyncio.create_task(show_frame(queue_)) await asyncio.wait(task1) if cv2.waitKey(1) == 27: break else: await asyncio.sleep(0.01) async def run(loop_, queue_, cap_): producer_task = asyncio.create_task(produce(loop_, queue_, cap_)) consumer_task = asyncio.create_task(consume(queue_)) await asyncio.gather(producer_task, consumer_task) if __name__ == '__main__': # config = '/home/user/Repo/mmaction2/configs/recognition/i3d/i3d_r50_video_inference_32x2x1_100e_kinetics400_rgb.py' # checkpoint = '/home/user/Repo/mmaction2/checkpoints/i3d_r50_video_32x2x1_100e_kinetics400_rgb_20200826-e31c6f52.pth' # device = torch.device('cuda:0') # model = init_recognizer(config, checkpoint, device=device, use_frames=False) video_path = 'replace_with_your_video.mp4' model_input_height = 256 model_input_width = 340 # target_layer_name = 'backbone/layer4/1/relu' # gradcam = GradCAM(model, target_layer_name) cap = cv2.VideoCapture(video_path) width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # float height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # float frame_buffer = np.zeros((1, 1, 3, 32, model_input_height, model_input_width)) display_buffer = np.zeros((32, model_input_height, model_input_width, 3)) # (32, 256, 340, 3) loop = asyncio.get_event_loop() queue = asyncio.LifoQueue(maxsize=2) try: loop.run_until_complete(run(loop_=loop, queue_=queue, cap_=cap)) finally: print("shutdown service") loop.close()  But when i run it, it reports following error : concurrent.futures.process._RemoteTraceback: """ Traceback (most recent call last): File "/home/user/miniconda3/lib/python3.7/concurrent/futures/process.py", line 205, in _sendback_result exception=exception)) File "/home/user/miniconda3/lib/python3.7/multiprocessing/queues.py", line 358, in put obj = _ForkingPickler.dumps(obj) File "/home/user/miniconda3/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) TypeError: can't pickle coroutine objects """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/user/Repo/Python-AI-Action-Utils/temp2.py", line 120, in <module> loop.run_until_complete(run(loop_=loop, queue_=queue, cap_=cap)) File "/home/user/miniconda3/lib/python3.7/asyncio/base_events.py", line 587, in run_until_complete return future.result() File "/home/user/Repo/Python-AI-Action-Utils/temp2.py", line 94, in run await asyncio.gather(producer_task, consumer_task) File "/home/user/Repo/Python-AI-Action-Utils/temp2.py", line 76, in produce display_buffer = await asyncio.create_task(run_blocking_func(loop_, queue_,frame_buffer)) File "/home/user/Repo/Python-AI-Action-Utils/temp2.py", line 42, in run_blocking_func frame = await loop_.run_in_executor(pool, blocking_func) TypeError: can't pickle coroutine objects Task was destroyed but it is pending! task: <Task pending coro=<consume() running at /home/user/Repo/Python-AI-Action-Utils/temp2.py:88> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x7f7cf1418cd0>()]> cb=[gather.<locals>._done_callback() at /home/user/miniconda3/lib/python3.7/asyncio/tasks.py:691]> Process finished with exit code 1 

Answer: If you use run_in_executor, target function should not be async. You need to remove async keyword before def inference().