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global: inference debug

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hofee
2024-11-01 21:58:44 +00:00
parent ecd4cfa806
commit 982a3b9b60
4 changed files with 17 additions and 16 deletions
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22
runners/inferencer.py
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@@ -72,7 +72,7 @@ class Inferencer(Runner):
data = test_set.__getitem__(i)
status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
output = self.predict_sequence(data)
self.save_inference_result(test_set_name, data["scene_name"][0], output)
self.save_inference_result(test_set_name, data["scene_name"], output)
status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
@@ -107,6 +107,7 @@ class Inferencer(Runner):
retry_no_pts_pose = []
retry = 0
pred_cr_seq = [last_pred_cr]
success = 0
while len(pred_cr_seq) < max_iter and retry < max_retry:
output = self.pipeline(input_data)
@@ -132,11 +133,13 @@ class Inferencer(Runner):
retry += 1
continue
pred_cr, new_added_pts_num = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
print(pred_cr, last_pred_cr, " max: ", data["seq_max_coverage_rate"])
if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
print("max coverage rate reached!: ", pred_cr)
if new_added_pts_num < 10:
success += 1
elif new_added_pts_num < 10:
print("min added pts num reached!: ", new_added_pts_num)
if pred_cr <= last_pred_cr + cr_increase_threshold:
retry += 1
@@ -156,32 +159,29 @@ class Inferencer(Runner):
random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
if success > 3:
break
last_pred_cr = pred_cr
input_data["scanned_pts"] = input_data["scanned_pts"][0].cpu().numpy().tolist()
input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
result = {
"pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
"pts_seq": input_data["scanned_pts"],
"combined_scanned_pts": input_data["combined_scanned_pts"],
"target_pts_seq": scanned_view_pts,
"coverage_rate_seq": pred_cr_seq,
"max_coverage_rate": data["max_coverage_rate"][0],
"max_coverage_rate": data["seq_max_coverage_rate"],
"pred_max_coverage_rate": max(pred_cr_seq),
"scene_name": scene_name,
"retry_no_pts_pose": retry_no_pts_pose,
"retry_duplication_pose": retry_duplication_pose,
"best_seq_len": data["best_seq_len"][0],
"best_seq_len": data["best_seq_len"],
}
self.stat_result[scene_name] = {
"max_coverage_rate": data["max_coverage_rate"][0],
"success_rate": max(pred_cr_seq)/ data["max_coverage_rate"][0],
"coverage_rate_seq": pred_cr_seq,
"pred_max_coverage_rate": max(pred_cr_seq),
"pred_seq_len": len(pred_cr_seq),
}
print('success rate: ', max(pred_cr_seq) / data["max_coverage_rate"][0])
print('success rate: ', max(pred_cr_seq))
return result