rts/game_CF/game.py [30:81]:
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            reply=None
        )

nIter = 5000
elapsed_wait_only = 0

import pickle
import argparse

if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    loader = Loader()
    args = ArgsProvider.Load(parser, [loader])

    def actor(batch):
        '''
        import pdb
        pdb.set_trace()
        pickle.dump(utils_elf.to_numpy(sel), open("tmp%d.bin" % k, "wb"), protocol=2)
        '''
        return dict(a=[0]*batch["s"].size(1))

    GC = loader.initialize()
    GC.reg_callback("actor", actor)

    before = datetime.now()
    GC.Start()

    import tqdm
    for k in tqdm.trange(nIter):
        b = datetime.now()
        GC.Run()
        elapsed_wait_only += (datetime.now() - b).total_seconds() * 1000
        #img = np.array(infos[0].data.image, copy=False)

    elapsed = (datetime.now() - before).total_seconds() * 1000
    print("elapsed = %.4lf ms, elapsed_wait_only = %.4lf" % (elapsed, elapsed_wait_only))
    GC.PrintSummary()
    GC.Stop()

    # Compute the statistics.
    per_loop = elapsed / nIter
    per_wait = elapsed_wait_only / nIter
    per_frame_loop_n_cpu = per_loop / args.batchsize
    per_frame_wait_n_cpu = per_wait / args.batchsize

    fps_loop = 1000 / per_frame_loop_n_cpu * args.frame_skip
    fps_wait = 1000 / per_frame_wait_n_cpu * args.frame_skip

    print("Time[Loop]: %.6lf ms / loop, %.6lf ms / frame_loop_n_cpu, %.2f FPS" % (per_loop, per_frame_loop_n_cpu, fps_loop))
    print("Time[Wait]: %.6lf ms / wait, %.6lf ms / frame_wait_n_cpu, %.2f FPS" % (per_wait, per_frame_wait_n_cpu, fps_wait))
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rts/game_TD/game.py [30:81]:
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            reply=None
        )

nIter = 5000
elapsed_wait_only = 0

import pickle
import argparse

if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    loader = Loader()
    args = ArgsProvider.Load(parser, [loader])

    def actor(batch):
        '''
        import pdb
        pdb.set_trace()
        pickle.dump(utils_elf.to_numpy(sel), open("tmp%d.bin" % k, "wb"), protocol=2)
        '''
        return dict(a=[0]*batch["s"].size(1))

    GC = loader.initialize()
    GC.reg_callback("actor", actor)

    before = datetime.now()
    GC.Start()

    import tqdm
    for k in tqdm.trange(nIter):
        b = datetime.now()
        GC.Run()
        elapsed_wait_only += (datetime.now() - b).total_seconds() * 1000
        #img = np.array(infos[0].data.image, copy=False)

    elapsed = (datetime.now() - before).total_seconds() * 1000
    print("elapsed = %.4lf ms, elapsed_wait_only = %.4lf" % (elapsed, elapsed_wait_only))
    GC.PrintSummary()
    GC.Stop()

    # Compute the statistics.
    per_loop = elapsed / nIter
    per_wait = elapsed_wait_only / nIter
    per_frame_loop_n_cpu = per_loop / args.batchsize
    per_frame_wait_n_cpu = per_wait / args.batchsize

    fps_loop = 1000 / per_frame_loop_n_cpu * args.frame_skip
    fps_wait = 1000 / per_frame_wait_n_cpu * args.frame_skip

    print("Time[Loop]: %.6lf ms / loop, %.6lf ms / frame_loop_n_cpu, %.2f FPS" % (per_loop, per_frame_loop_n_cpu, fps_loop))
    print("Time[Wait]: %.6lf ms / wait, %.6lf ms / frame_wait_n_cpu, %.2f FPS" % (per_wait, per_frame_wait_n_cpu, fps_wait))
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