global_only: debug
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@ -3,11 +3,11 @@ runner:
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general:
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general:
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seed: 0
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seed: 0
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device: cuda
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device: cuda
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cuda_visible_devices: "1"
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cuda_visible_devices: "0"
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parallel: False
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parallel: False
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experiment:
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experiment:
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name: debug
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name: overfit_ab_global_only
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root_dir: "experiments"
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root_dir: "experiments"
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use_checkpoint: False
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use_checkpoint: False
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epoch: -1 # -1 stands for last epoch
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epoch: -1 # -1 stands for last epoch
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@ -28,50 +28,50 @@ runner:
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#- OmniObject3d_test
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#- OmniObject3d_test
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- OmniObject3d_val
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- OmniObject3d_val
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pipeline: nbv_reconstruction_global_pts_n_num_pipeline
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pipeline: nbv_reconstruction_pipeline
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dataset:
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dataset:
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OmniObject3d_train:
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OmniObject3d_train:
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root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new"
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root_dir: "/data/hofee/nbv_rec_part2_preprocessed"
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model_dir: "../data/scaled_object_meshes"
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model_dir: "../data/scaled_object_meshes"
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source: nbv_reconstruction_dataset
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source: nbv_reconstruction_dataset
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split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt"
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split_file: "/data/hofee/data/sample.txt"
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type: train
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type: train
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cache: True
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cache: True
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ratio: 1
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ratio: 1
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batch_size: 160
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batch_size: 80
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num_workers: 16
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num_workers: 16
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pts_num: 8192
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pts_num: 8192
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load_from_preprocess: True
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load_from_preprocess: True
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OmniObject3d_test:
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OmniObject3d_test:
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root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new"
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root_dir: "/data/hofee/nbv_rec_part2_preprocessed"
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model_dir: "../data/scaled_object_meshes"
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model_dir: "../data/scaled_object_meshes"
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source: nbv_reconstruction_dataset
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source: nbv_reconstruction_dataset
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split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt"
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split_file: "/data/hofee/data/sample.txt"
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type: test
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type: test
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cache: True
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cache: True
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filter_degree: 75
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filter_degree: 75
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eval_list:
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eval_list:
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- pose_diff
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- pose_diff
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ratio: 0.05
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ratio: 1
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batch_size: 160
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batch_size: 80
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num_workers: 12
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num_workers: 12
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pts_num: 8192
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pts_num: 8192
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load_from_preprocess: True
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load_from_preprocess: True
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OmniObject3d_val:
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OmniObject3d_val:
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root_dir: "/home/data/hofee/project/nbv_rec/data/sample_for_training_new"
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root_dir: "/data/hofee/nbv_rec_part2_preprocessed"
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model_dir: "../data/scaled_object_meshes"
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model_dir: "../data/scaled_object_meshes"
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source: nbv_reconstruction_dataset
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source: nbv_reconstruction_dataset
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split_file: "/home/data/hofee/project/nbv_rec/data/sample.txt"
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split_file: "/data/hofee/data/sample.txt"
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type: test
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type: test
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cache: True
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cache: True
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filter_degree: 75
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filter_degree: 75
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eval_list:
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eval_list:
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- pose_diff
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- pose_diff
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ratio: 0.005
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ratio: 1
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batch_size: 160
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batch_size: 80
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num_workers: 12
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num_workers: 12
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pts_num: 8192
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pts_num: 8192
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load_from_preprocess: True
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load_from_preprocess: True
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@ -34,7 +34,7 @@ class NBVReconstructionDataset(BaseDataset):
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#self.model_dir = config["model_dir"]
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#self.model_dir = config["model_dir"]
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self.filter_degree = config["filter_degree"]
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self.filter_degree = config["filter_degree"]
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if self.type == namespace.Mode.TRAIN:
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if self.type == namespace.Mode.TRAIN:
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scale_ratio = 100
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scale_ratio = 50
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self.datalist = self.datalist*scale_ratio
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self.datalist = self.datalist*scale_ratio
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if self.cache:
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if self.cache:
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expr_root = ConfigManager.get("runner", "experiment", "root_dir")
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expr_root = ConfigManager.get("runner", "experiment", "root_dir")
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@ -206,14 +206,9 @@ class NBVReconstructionDataset(BaseDataset):
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collate_data["combined_scanned_pts"] = torch.stack(
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collate_data["combined_scanned_pts"] = torch.stack(
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[torch.tensor(item["combined_scanned_pts"]) for item in batch]
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[torch.tensor(item["combined_scanned_pts"]) for item in batch]
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)
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)
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collate_data["scanned_pts_mask"] = torch.stack(
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[torch.tensor(item["scanned_pts_mask"]) for item in batch]
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)
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for key in batch[0].keys():
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for key in batch[0].keys():
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if key not in [
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if key not in [
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"scanned_pts",
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"scanned_pts",
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"scanned_pts_mask",
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"scanned_n_to_world_pose_9d",
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"scanned_n_to_world_pose_9d",
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"best_to_world_pose_9d",
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"best_to_world_pose_9d",
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"combined_scanned_pts",
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"combined_scanned_pts",
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@ -20,8 +20,8 @@ class NBVReconstructionPipeline(nn.Module):
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self.pose_encoder = ComponentFactory.create(
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self.pose_encoder = ComponentFactory.create(
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namespace.Stereotype.MODULE, self.module_config["pose_encoder"]
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namespace.Stereotype.MODULE, self.module_config["pose_encoder"]
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)
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)
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self.transformer_seq_encoder = ComponentFactory.create(
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self.seq_encoder = ComponentFactory.create(
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namespace.Stereotype.MODULE, self.module_config["transformer_seq_encoder"]
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namespace.Stereotype.MODULE, self.module_config["seq_encoder"]
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)
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)
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self.view_finder = ComponentFactory.create(
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self.view_finder = ComponentFactory.create(
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namespace.Stereotype.MODULE, self.module_config["view_finder"]
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namespace.Stereotype.MODULE, self.module_config["view_finder"]
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@ -107,7 +107,7 @@ class NBVReconstructionPipeline(nn.Module):
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seq_embedding = pose_feat_seq
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seq_embedding = pose_feat_seq
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embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp))
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embedding_list_batch.append(seq_embedding) # List(B): Tensor(S x (Dp))
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seq_feat = self.transformer_seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
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seq_feat = self.seq_encoder.encode_sequence(embedding_list_batch) # Tensor(B x Ds)
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main_feat = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg))
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main_feat = torch.cat([seq_feat, global_scanned_feat], dim=-1) # Tensor(B x (Ds+Dg))
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if torch.isnan(main_feat).any():
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if torch.isnan(main_feat).any():
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