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作者您好: 以下是我遇到的问题,望能得到解答。
def ddim_sample(self, batched_inputs, backbone_feats, images_whwh, images, clip_denoised=True, do_postprocess=True): batch = images_whwh.shape[0] shape = (batch, self.num_proposals, 4) total_timesteps, sampling_timesteps, eta, objective = self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps times = torch.linspace(-1, total_timesteps - 1, steps=sampling_timesteps + 1) times = list(reversed(times.int().tolist())) time_pairs = list(zip(times[:-1], times[1:])) # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]
这段程序中关于sampling_timesteps设置为1之后,所生成的times似乎是(999,-1)这样的结果,这样的话在下面的for循环中好像就无法实现逐步去噪的作用,循环语句中的内容只会执行一次。按我的理解是,生成#语句中那样的时间步序列,然后循环很多次以达到一个逐步去噪的作用。 是我的理解错误么?望百忙之中回复,谢谢~
The text was updated successfully, but these errors were encountered:
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作者您好:
以下是我遇到的问题,望能得到解答。
这段程序中关于sampling_timesteps设置为1之后,所生成的times似乎是(999,-1)这样的结果,这样的话在下面的for循环中好像就无法实现逐步去噪的作用,循环语句中的内容只会执行一次。按我的理解是,生成#语句中那样的时间步序列,然后循环很多次以达到一个逐步去噪的作用。
是我的理解错误么?望百忙之中回复,谢谢~
The text was updated successfully, but these errors were encountered: