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hofee 2024-11-04 23:49:12 +08:00
parent 2b7243d1be
commit 5bcd0fc6e3
2 changed files with 14 additions and 18 deletions
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24
configs/local/inference_config.yaml
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@ -20,18 +20,18 @@ runner:
voxel_size: 0.003
dataset:
OmniObject3d_train:
root_dir: "C:\\Document\\Datasets\\inference_test"
model_dir: "C:\\Document\\Datasets\\scaled_object_meshes"
source: seq_reconstruction_dataset_preprocessed
split_file: "C:\\Document\\Datasets\\data_list\\sample.txt"
type: test
filter_degree: 75
ratio: 1
batch_size: 1
num_workers: 12
pts_num: 8192
load_from_preprocess: True
# OmniObject3d_train:
# root_dir: "C:\\Document\\Datasets\\inference_test1"
# model_dir: "C:\\Document\\Datasets\\scaled_object_meshes"
# source: seq_reconstruction_dataset_preprocessed
# split_file: "C:\\Document\\Datasets\\data_list\\sample.txt"
# type: test
# filter_degree: 75
# ratio: 1
# batch_size: 1
# num_workers: 12
# pts_num: 8192
# load_from_preprocess: True
OmniObject3d_test:
root_dir: "C:\\Document\\Datasets\\inference_test"

8
runners/inferencer.py
View File

@ -87,7 +87,7 @@ class Inferencer(Runner):
status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
def predict_sequence(self, data, cr_increase_threshold=0.001, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 7):
def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 7):
scene_name = data["scene_name"]
Log.info(f"Processing scene: {scene_name}")
status_manager.set_status("inference", "inferencer", "scene", scene_name)
@ -106,7 +106,6 @@ class Inferencer(Runner):
''' data for inference '''
input_data = {}
scanned_pts = []
input_data["combined_scanned_pts"] = torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)
input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)]
input_data["mode"] = namespace.Mode.TEST
@ -119,7 +118,6 @@ class Inferencer(Runner):
scanned_view_pts = [first_frame_target_pts]
history_indices = [first_frame_scan_points_indices]
last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
scanned_pts.append(first_frame_target_pts)
retry_duplication_pose = []
retry_no_pts_pose = []
retry_overlap_pose = []
@ -154,7 +152,6 @@ class Inferencer(Runner):
retry_overlap_pose.append(pred_pose.cpu().numpy().tolist())
continue
scanned_pts.append(new_target_pts)
history_indices.append(new_scan_points_indices)
end_time = time.time()
print(f"Time taken for rendering: {end_time - start_time} seconds")
@ -182,7 +179,7 @@ class Inferencer(Runner):
print("max coverage rate reached!: ", pred_cr)
success += 1
elif new_added_pts_num < 5:
success += 1
#success += 1
print("min added pts num reached!: ", new_added_pts_num)
if pred_cr <= last_pred_cr + cr_increase_threshold:
retry += 1
@ -208,7 +205,6 @@ class Inferencer(Runner):
input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
result = {
"scanned_pts": scanned_pts,
"pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
"combined_scanned_pts": input_data["combined_scanned_pts"],
"target_pts_seq": scanned_view_pts,