fix a little bug
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90f00a16ad
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079d8cf23a
@ -166,12 +166,12 @@ class YopoNet:
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if self.verbose:
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self.time_interpolation = self.time_interpolation + (time.time() - start)
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self.count_interpolation = self.count_interpolation + 1
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print("interpolation time:", self.time_interpolation / self.count_interpolation)
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print("Time Consuming: interpolation:", self.time_interpolation / self.count_interpolation)
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# cv2.imshow("1", depth_[0][0])
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# cv2.waitKey(1)
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self.new_depth = True
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self.depth = depth_.astype(np.float32)
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self.new_depth = True
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def callback_set_goal(self, data):
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self.goal = np.asarray([data.pose.position.x, data.pose.position.y, 2])
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@ -200,9 +200,11 @@ class YopoNet:
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endstate_pred = endstate_pred.squeeze()
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time3 = time.time()
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else:
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time1 = time.time()
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endstate_pred, score_pred = self.policy.predict(depth, obs_norm_input, return_all_preds=self.visualize)
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endstate_pred = endstate_pred.cpu().numpy().squeeze()
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score_pred = score_pred.cpu().numpy()
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time2 = time3 = time.time()
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# Transform the prediction(body frame) to the world frame with the attitude in inference
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# Replacing PyTorch calculations on CUDA with NumPy calculations on the CPU (speed increased by 10x)
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@ -230,8 +232,8 @@ class YopoNet:
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self.time_forward = self.time_forward + (time2 - time1)
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self.time_process = self.time_process + (time3 - time2)
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self.count = self.count + 1
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print("time forward:", self.time_forward / self.count, "process:", self.time_process / self.count,
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"prepare:", self.time_prepare / self.count)
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print("Time Consuming: prepare:", self.time_prepare / self.count, "; forward:", self.time_forward / self.count,
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"; process:", self.time_process / self.count)
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# publish
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if not self.visualize:
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