global_and_partial_global: upd
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@ -6,17 +6,17 @@ runner:
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cuda_visible_devices: "0,1,2,3,4,5,6,7"
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cuda_visible_devices: "0,1,2,3,4,5,6,7"
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experiment:
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experiment:
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name: debug
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name: server_split_dataset
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root_dir: "experiments"
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root_dir: "experiments"
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split: #
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split: #
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root_dir: "/data/hofee/data/packed_preprocessed_data"
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root_dir: "/data/hofee/data/new_full_data"
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type: "unseen_instance" # "unseen_category"
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type: "unseen_instance" # "unseen_category"
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datasets:
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datasets:
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OmniObject3d_train:
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OmniObject3d_train:
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path: "/data/hofee/data/OmniObject3d_train.txt"
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path: "/data/hofee/data/new_full_data_list/OmniObject3d_train.txt"
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ratio: 0.9
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ratio: 0.9
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OmniObject3d_test:
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OmniObject3d_test:
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path: "/data/hofee/data/OmniObject3d_test.txt"
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path: "/data/hofee/data/new_full_data_list/OmniObject3d_test.txt"
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ratio: 0.1
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ratio: 0.1
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@ -54,10 +54,7 @@ class NBVReconstructionPipeline(nn.Module):
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return perturbed_x, random_t, target_score, std
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return perturbed_x, random_t, target_score, std
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def forward_train(self, data):
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def forward_train(self, data):
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start_time = time.time()
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main_feat = self.get_main_feat(data)
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main_feat = self.get_main_feat(data)
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end_time = time.time()
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print("get_main_feat time: ", end_time - start_time)
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""" get std """
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""" get std """
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best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
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best_to_world_pose_9d_batch = data["best_to_world_pose_9d"]
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perturbed_x, random_t, target_score, std = self.pertube_data(
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perturbed_x, random_t, target_score, std = self.pertube_data(
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