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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+correspondence/SegSwap/data/input2.mp4 filter=lfs diff=lfs merge=lfs -text
+correspondence/SegSwap/input.mp4 filter=lfs diff=lfs merge=lfs -text
+correspondence/XMem/PSALM/images/teaser.png filter=lfs diff=lfs merge=lfs -text
+seg_dis_new.log filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+.vim-arsync
+*.pyc

1.log

@@ -0,0 +1,3 @@
+820
+65
+['f2f93854-2634-449c-b68e-aebf4743ac9f', '7d59164c-e0bc-4ae0-95c9-733e4c8b0d6a', 'cace80a1-42df-4cf4-a1ec-80647638a443', '8fa671be-2624-4783-8572-5f4b7722b6c0', 'ca1434ea-b787-44ad-a9da-e0f7d5167a35', 'cfd2c825-45d1-4e59-b33f-b6dff8c174c8', '39d48b6a-66e8-4bbb-a596-4461b601cabc', 'f4dead01-fa3d-4aa5-8b59-13a0d9186dd2', '4cf43506-d0a6-4c42-9136-adb2ecd57411', '89815623-8ece-4e3c-8879-f1f32b299527', '5fd383f1-c8bb-42d7-b98b-7418d99d9bb4', 'b1b794e8-7839-46ab-b05f-f4b1c16d5420', 'b7dbb47c-d850-4853-b434-7b20519ea9e5', '636eaa0b-d65d-4b25-bbdd-1065f84ef89e', 'd2b0ee95-2a76-4b69-aebf-3c7e553f8e2b', 'cea1b20b-6e18-4bb6-87e0-164a2b8c3dc0', 'fba2c124-99ec-40ed-8d6a-46808afe6d98', '549e5b97-f93a-4500-8f02-5be13017dce5', '6cbfa460-72a7-4038-b6af-4305a1cc05dd', '319a9983-f70c-4224-a3a7-33338c8a9f35', 'bcfb6839-3c09-4d42-9d9c-59042f6ab721', '3ca2798d-cfe3-46c6-a8bc-cc4689bd6d75', 'aa40670e-4487-4f60-a27b-26f7372ef8e7', '389cfa3f-3a4c-4b8f-9535-d7c95ffd594c', '214bdc0d-fa50-4a84-b771-c0a7bdeafadc', '08504348-4f72-477e-a08f-1050204ae55e', 'd35a162e-c38e-4017-94d1-539f26651115', '925ebe22-f97a-4e79-aed3-9873cf461c3c', 'c0b7d130-2004-4450-ad94-ea3167bd9fab', 'f1ce9be1-b623-4ce4-84e2-37e9f88eea86', 'c288cb15-81c7-4463-814f-959c12740499', 'b69de073-c157-4cff-8eba-97bfc7baa012', '289c4873-5bf9-41b9-b784-aba52b54cd4d', '0bcb8b46-cc45-4bcb-a627-85633e54e060', '819780c3-97c0-4ac6-b37f-8c66abf8167d', '9512a137-40b6-49c6-a03d-a3340b9dd277', '393e9b60-504c-4cc7-a90b-eb78dd62d5ff', 'fb09baec-1a5a-40b4-8d72-e581c93fbd77', '3042b472-99cc-4407-a79e-f76916b95737', '4939903a-2c73-4633-ae15-618be39990e7', 'ced0e340-b958-4505-badc-c8c2f256c145', 'e53ae33b-61b6-4c3e-8be0-5696f961704b', '759aa03c-c8e1-4fcb-8817-85948100ed33', '31df8578-1fd0-4406-9008-900a88f7990a', '9506c70a-1639-42ea-bbfc-2b9c0f8c9394', 'e46f9a53-7625-4827-8b92-79c958d3524e', '3125dca7-b99e-4b2b-8844-2d912619b353', 'b65a60e0-f224-4b3d-bf78-ba44e12c4ac1', 'c53daadd-09f0-485b-a51c-1d5679f5fb09', 'b378d186-0587-40ed-afdf-875e6dfb5876', '8000dfb2-accb-4bb8-abfb-cc2d677d0b2f', 'ff93ae48-0daa-418c-9d5c-b5b0b6d23efb', '6d0a7c80-ae8c-4673-8f70-c09fd6fbebe8', 'f0cd03a5-9cd8-4510-87c1-a5c493197b75', 'c16175f5-f990-48a1-9dcc-6a385f108687', '816dd81e-93b1-433c-83ff-264ae404a3bf', 'f8bed5fe-3e09-4885-9539-edb4d5b2279a', '601c9c61-fc2b-4ac2-b3c4-dda557c2563b', '56c35d79-acb1-47a1-8590-7e5cb2585ee5', 'd5193bae-a7f5-4e8a-9c96-09f557c7ea9d', '8d7646a3-ce7b-425d-b16e-9a63a1166576', '0b89efcd-59bf-4f0b-a81a-50dee0b79982', '9681b4c6-9713-4bb3-aa9a-7df7daa4e74d', '69fac17f-6527-493d-8dac-cd3bb61ce23e', '5ee00a17-171f-46a4-927b-3aa9d0fe176e']

2.log

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+201
+0
+[]

3.log

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+2024-09-02 06:43:53,395 INFO Namespace(n_epoch=250, lr_schedule=[50], lr=0.0002, gpu='0', out_dir='/data/work-gcp-europe-west4-a/yuqian_fu/Ego/checkpoints/egoexo_v2_480x480', train_dir=['/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap'], prob_dir=[0.5, 0.5], batch_pos=32, batch_neg=15, feat_pth='../evalBrueghel/Moco_resnet50_feat_1Scale_640p.pkl', warp_mask=False, warmUpIter=1000, resume_pth=None, resume_epoch=0, mode='small', pos_weight=0.1, feat_weight=1, dropout=0.1, activation='relu', prob_style=0.5, layer_type=['I', 'C', 'I', 'C', 'I', 'N'], drop_feat=0.1, tps_grid=[4, 6], eta_corr=8, iter_epoch=1000, iter_epoch_val=100, weight_decay=0, reverse=False)
+2024-09-02 06:43:53,396 INFO Load MocoV2 pre-trained ResNet-50 feature...
+LOADING:  train_egoexo_pairs.json
+LOADING:  val_egoexo_pairs.json
+
  0%|          | 0/1000 [00:00<?, ?it/s]
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+Traceback (most recent call last):
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/train/Main.py", line 188, in <module>
+    backbone, netEncoder, optimizer, history = Train.trainEpoch(trainLoader, backbone, netEncoder, optimizer, history, Loss, ClsLoss, args.batch_pos, args.batch_neg, args.warp_mask, logger, args.eta_corr, args.warmUpIter, 0, args.lr, writer, warmup=True)
+                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/train/Train.py", line 80, in trainEpoch
+    O1, O2, O3 = netEncoder(X, Y, FMTX, RS, RT)
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 342, in forward
+    outx, outy, out_cls = self.net(x, y, fmask, x_mask, y_mask)
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 291, in forward
+    featx, featy, x_mask, y_mask = self.encoder_blocks[i](featx, featy, featmask, x_mask, y_mask)
+                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 205, in forward
+    featx, featy, x_mask, y_mask = self.layer1(featx, featy, featmask, x_mask, y_mask)
+                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 105, in forward
+    output = self.inner_encoder_layer(output, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 591, in forward
+    x = self.norm1(x + self._sa_block(x, src_mask, src_key_padding_mask, is_causal=is_causal))
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 599, in _sa_block
+    x = self.self_attn(x, x, x,
+        ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/modules/activation.py", line 1205, in forward
+    attn_output, attn_output_weights = F.multi_head_attention_forward(
+                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-zap7rdp2jlp7/lib/python3.11/site-packages/torch/nn/functional.py", line 5373, in multi_head_attention_forward
+    attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 792.00 MiB (GPU 0; 21.95 GiB total capacity; 20.03 GiB already allocated; 790.12 MiB free; 20.94 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+srun: error: gcpl4-eu-0: task 0: Exited with exit code 1

8.log

@@ -0,0 +1,110 @@
+WARNING:torch.distributed.run:
+*****************************************
+Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+*****************************************
+WARNING:torch.distributed.run:
+*****************************************
+Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+*****************************************
+[W socket.cpp:426] [c10d] The server socket has failed to bind to [::]:12345 (errno: 98 - Address already in use).
+[W socket.cpp:426] [c10d] The server socket has failed to bind to 0.0.0.0:12345 (errno: 98 - Address already in use).
+[E socket.cpp:462] [c10d] The server socket has failed to listen on any local network address.
+Traceback (most recent call last):
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/bin/torchrun", line 8, in <module>
+    sys.exit(main())
+             ^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
+    return f(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/run.py", line 794, in main
+    run(args)
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/run.py", line 785, in run
+    elastic_launch(
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/launcher/api.py", line 241, in launch_agent
+    result = agent.run()
+             ^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/metrics/api.py", line 129, in wrapper
+    result = f(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/agent/server/api.py", line 723, in run
+    result = self._invoke_run(role)
+             ^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/agent/server/api.py", line 858, in _invoke_run
+    self._initialize_workers(self._worker_group)
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/metrics/api.py", line 129, in wrapper
+    result = f(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/agent/server/api.py", line 692, in _initialize_workers
+    self._rendezvous(worker_group)
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/metrics/api.py", line 129, in wrapper
+    result = f(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/agent/server/api.py", line 546, in _rendezvous
+    store, group_rank, group_world_size = spec.rdzv_handler.next_rendezvous()
+                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py", line 55, in next_rendezvous
+    self._store = TCPStore(  # type: ignore[call-arg]
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+RuntimeError: The server socket has failed to listen on any local network address. The server socket has failed to bind to [::]:12345 (errno: 98 - Address already in use). The server socket has failed to bind to 0.0.0.0:12345 (errno: 98 - Address already in use).
+srun: error: gcpl4-eu-0: task 0: Exited with exit code 1
+2024-09-04 08:00:56,890 INFO Namespace(n_epoch=250, lr_schedule=[50], lr=0.0002, gpu='0', out_dir='/data/work-gcp-europe-west4-a/yuqian_fu/Ego/checkpoints/egoexo_v2_480x480', train_dir=['/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap'], prob_dir=[0.5, 0.5], batch_pos=32, batch_neg=15, feat_pth='../evalBrueghel/Moco_resnet50_feat_1Scale_640p.pkl', warp_mask=False, warmUpIter=1000, resume_pth='/data/work-gcp-europe-west4-a/yuqian_fu/Ego/checkpoints/egoexo_v2_480x480/netLast.pth', resume_epoch=0, mode='small', pos_weight=0.1, feat_weight=1, dropout=0.1, activation='relu', prob_style=0.5, layer_type=['I', 'C', 'I', 'C', 'I', 'N'], drop_feat=0.1, tps_grid=[4, 6], eta_corr=8, iter_epoch=1000, iter_epoch_val=100, weight_decay=0, reverse=False)
+2024-09-04 08:00:56,890 INFO Load MocoV2 pre-trained ResNet-50 feature...
+2024-09-04 08:00:56,892 INFO Namespace(n_epoch=250, lr_schedule=[50], lr=0.0002, gpu='0', out_dir='/data/work-gcp-europe-west4-a/yuqian_fu/Ego/checkpoints/egoexo_v2_480x480', train_dir=['/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap'], prob_dir=[0.5, 0.5], batch_pos=32, batch_neg=15, feat_pth='../evalBrueghel/Moco_resnet50_feat_1Scale_640p.pkl', warp_mask=False, warmUpIter=1000, resume_pth='/data/work-gcp-europe-west4-a/yuqian_fu/Ego/checkpoints/egoexo_v2_480x480/netLast.pth', resume_epoch=0, mode='small', pos_weight=0.1, feat_weight=1, dropout=0.1, activation='relu', prob_style=0.5, layer_type=['I', 'C', 'I', 'C', 'I', 'N'], drop_feat=0.1, tps_grid=[4, 6], eta_corr=8, iter_epoch=1000, iter_epoch_val=100, weight_decay=0, reverse=False)
+2024-09-04 08:00:56,892 INFO Load MocoV2 pre-trained ResNet-50 feature...
+LOADING:  train_egoexo_pairs.json
+LOADING:  train_egoexo_pairs.json
+LOADING: LOADING:   val_egoexo_pairs.jsonval_egoexo_pairs.json
+
+Traceback (most recent call last):
+Traceback (most recent call last):
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/train/Main_dis.py", line 186, in <module>
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/train/Main_dis.py", line 186, in <module>
+    trainLoader.sampler = DistributedSampler(trainLoader.dataset, num_replicas=world_size, rank=rank)
+                          ^^^^^^^^^^^^^^^^^^
+NameError: name 'DistributedSampler' is not defined
+    trainLoader.sampler = DistributedSampler(trainLoader.dataset, num_replicas=world_size, rank=rank)
+                          ^^^^^^^^^^^^^^^^^^
+NameError: name 'DistributedSampler' is not defined
+ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 766624) of binary: /scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/bin/python3.11
+Traceback (most recent call last):
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/bin/torchrun", line 8, in <module>
+    sys.exit(main())
+             ^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
+    return f(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/run.py", line 794, in main
+    run(args)
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/run.py", line 785, in run
+    elastic_launch(
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
+    raise ChildFailedError(
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/train/Main_dis.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2024-09-04_08:02:09
+  host      : gcpl4-eu-0.slurm.insait.ai
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 766625)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2024-09-04_08:02:09
+  host      : gcpl4-eu-0.slurm.insait.ai
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 766624)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
+srun: error: gcpl4-eu-0: task 1: Exited with exit code 1

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+  j = inters / union
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+TOTAL EXISTENCE BALANCED ACC:  0.6156669615094792
+TOTAL IOU:  0.05312182000938763
+TOTAL LOCATION SCORE:  0.11091002580370038
+TOTAL SHAPE ACC:  0.1461757670000539

83062.log

@@ -0,0 +1,441 @@
+Solving dependencies
+Installing conda packages
+Empty environment created at prefix: /scratch/yuqian_fu/micromamba/envs/auto-z4wmiwft6kwh
+Transaction
+
+  Prefix: /scratch/yuqian_fu/micromamba/envs/auto-z4wmiwft6kwh
+
+
+
+  No specs added or removed.
+
+  Package               Version  Build               Channel                           Size
+─────────────────────────────────────────────────────────────────────────────────────────────
+  Install:
+─────────────────────────────────────────────────────────────────────────────────────────────
+
+  + _libgcc_mutex           0.1  conda_forge         conda-forge                           
+  + _openmp_mutex           4.5  2_gnu               conda-forge                           
+  + bzip2                 1.0.8  h4bc722e_7          conda-forge                           
+  + ca-certificates   2024.8.30  hbcca054_0          conda-forge                           
+  + ld_impl_linux-64       2.40  hf3520f5_7          conda-forge                           
+  + libexpat              2.6.3  h5888daf_0          conda-forge                           
+  + libffi                3.4.2  h7f98852_5          conda-forge                           
+  + libgcc               14.1.0  h77fa898_1          conda-forge                           
+  + libgcc-ng            14.1.0  h69a702a_1          conda-forge                           
+  + libgomp              14.1.0  h77fa898_1          conda-forge                           
+  + libnsl                2.0.1  hd590300_0          conda-forge                           
+  + libsqlite            3.46.1  hadc24fc_0          conda-forge                           
+  + libuuid              2.38.1  h0b41bf4_0          conda-forge                           
+  + libxcrypt            4.4.36  hd590300_1          conda-forge                           
+  + libzlib               1.3.1  h4ab18f5_1          conda-forge                           
+  + ncurses                 6.5  he02047a_1          conda-forge                           
+  + openssl               3.3.2  hb9d3cd8_0          conda-forge                           
+  + pip                    24.2  pyh8b19718_1        conda-forge                           
+  + python              3.11.10  hc5c86c4_0_cpython  conda-forge                           
+  + readline                8.2  h8228510_1          conda-forge                           
+  + setuptools           73.0.1  pyhd8ed1ab_0        conda-forge                           
+  + tk                   8.6.13  noxft_h4845f30_101  /work/conda/cache/conda-forge         
+  + tzdata                2024a  h8827d51_1          conda-forge                           
+  + wheel                0.44.0  pyhd8ed1ab_0        conda-forge                           
+  + xz                    5.2.6  h166bdaf_0          conda-forge                           
+
+  Summary:
+
+  Install: 25 packages
+
+  Total download: 0 B
+
+─────────────────────────────────────────────────────────────────────────────────────────────
+
+
+
+Transaction starting
+Linking _libgcc_mutex-0.1-conda_forge
+Linking ld_impl_linux-64-2.40-hf3520f5_7
+Linking ca-certificates-2024.8.30-hbcca054_0
+Linking libgomp-14.1.0-h77fa898_1
+Linking _openmp_mutex-4.5-2_gnu
+Linking libgcc-14.1.0-h77fa898_1
+Linking libgcc-ng-14.1.0-h69a702a_1
+Linking openssl-3.3.2-hb9d3cd8_0
+Linking libexpat-2.6.3-h5888daf_0
+Linking libffi-3.4.2-h7f98852_5
+Linking libzlib-1.3.1-h4ab18f5_1
+Linking libxcrypt-4.4.36-hd590300_1
+Linking bzip2-1.0.8-h4bc722e_7
+Linking ncurses-6.5-he02047a_1
+Linking xz-5.2.6-h166bdaf_0
+Linking libuuid-2.38.1-h0b41bf4_0
+Linking libnsl-2.0.1-hd590300_0
+Linking tk-8.6.13-noxft_h4845f30_101
+Linking libsqlite-3.46.1-hadc24fc_0
+Linking readline-8.2-h8228510_1
+Linking tzdata-2024a-h8827d51_1
+Linking python-3.11.10-hc5c86c4_0_cpython
+Linking pip-24.2-pyh8b19718_1
+Linking setuptools-73.0.1-pyhd8ed1ab_0
+Linking wheel-0.44.0-pyhd8ed1ab_0
+
+Transaction finished
+
+To activate this environment, use:
+
+    mamba activate auto-z4wmiwft6kwh
+
+Or to execute a single command in this environment, use:
+
+    mamba run -n auto-z4wmiwft6kwh mycommand
+
+Installing pip packages
+Collecting git+https://github.com/cheind/py-thin-plate-spline
+  Cloning https://github.com/cheind/py-thin-plate-spline to /tmp/pip-req-build-a_rro282
+  Running command git clone --filter=blob:none --quiet https://github.com/cheind/py-thin-plate-spline /tmp/pip-req-build-a_rro282
+  Resolved https://github.com/cheind/py-thin-plate-spline to commit f6995795397118b7d0ac01aecd3f39ffbfad9dee
+  Preparing metadata (setup.py): started
+  Preparing metadata (setup.py): finished with status 'done'
+Collecting absl-py==1.4.0
+  Using cached absl_py-1.4.0-py3-none-any.whl.metadata (2.3 kB)
+Collecting appdirs==1.4.4
+  Using cached appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)
+Collecting cachetools==5.3.1
+  Using cached cachetools-5.3.1-py3-none-any.whl.metadata (5.2 kB)
+Collecting certifi==2023.5.7
+  Using cached certifi-2023.5.7-py3-none-any.whl.metadata (2.2 kB)
+Collecting charset-normalizer==3.1.0
+  Using cached charset_normalizer-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (30 kB)
+Collecting click==8.1.4
+  Using cached click-8.1.4-py3-none-any.whl.metadata (3.0 kB)
+Collecting cmake==3.26.4
+  Using cached cmake-3.26.4-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (6.7 kB)
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+Using cached tensorboard_data_server-0.7.1-py3-none-manylinux2014_x86_64.whl (6.6 MB)
+Using cached tensorboard-2.13.0-py3-none-any.whl (5.6 MB)
+Using cached tensorboardX-2.6.1-py2.py3-none-any.whl (101 kB)
+Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
+Using cached tifffile-2023.9.26-py3-none-any.whl (222 kB)
+Using cached torch-2.0.1-cp311-cp311-manylinux1_x86_64.whl (619.9 MB)
+Using cached torchvision-0.15.2-cp311-cp311-manylinux1_x86_64.whl (6.0 MB)
+Using cached tqdm-4.65.0-py3-none-any.whl (77 kB)
+Using cached triton-2.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (63.3 MB)
+Using cached tzdata-2023.3-py2.py3-none-any.whl (341 kB)
+Using cached urllib3-1.26.16-py2.py3-none-any.whl (143 kB)
+Using cached wandb-0.15.5-py3-none-any.whl (2.1 MB)
+Using cached Werkzeug-2.3.6-py3-none-any.whl (242 kB)
+Using cached wheel-0.38.4-py3-none-any.whl (36 kB)
+Downloading gdown-5.2.0-py3-none-any.whl (18 kB)
+Downloading hickle-5.0.3-py3-none-any.whl (107 kB)
+Downloading opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.9 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 49.9/49.9 MB 188.3 MB/s eta 0:00:00
+Downloading progressbar2-4.5.0-py3-none-any.whl (57 kB)
+Using cached future-1.0.0-py3-none-any.whl (491 kB)
+Downloading h5py-3.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.4 MB)
+   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.4/5.4 MB 185.1 MB/s eta 0:00:00
+Using cached packaging-24.1-py3-none-any.whl (53 kB)
+Using cached platformdirs-4.3.2-py3-none-any.whl (18 kB)
+Using cached python_dateutil-2.9.0.post0-py2.py3-none-any.whl (229 kB)
+Downloading python_utils-3.8.2-py2.py3-none-any.whl (27 kB)
+Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
+Using cached traitlets-5.14.3-py3-none-any.whl (85 kB)
+Using cached typing_extensions-4.12.2-py3-none-any.whl (37 kB)
+Downloading beautifulsoup4-4.12.3-py3-none-any.whl (147 kB)
+Downloading PySocks-1.7.1-py3-none-any.whl (16 kB)
+Downloading soupsieve-2.6-py3-none-any.whl (36 kB)
+Building wheels for collected packages: thinplate
+  Building wheel for thinplate (setup.py): started
+  Building wheel for thinplate (setup.py): finished with status 'done'
+  Created wheel for thinplate: filename=thinplate-1.0.0-py3-none-any.whl size=6699 sha256=d4ffda8c168d40baa5ef3b0079bb50fbb08378232c5c286c1cfe14228a4fadb8
+  Stored in directory: /tmp/pip-ephem-wheel-cache-9hm560_h/wheels/8d/a9/10/26074e234ee6057094ebcf080ff2f898ab16f418672fab12ad
+Successfully built thinplate
+Installing collected packages: pytz, pathtools, mpmath, lit, cmake, appdirs, wheel, urllib3, tzdata, typing-extensions, traitlets, tqdm, tornado, threadpoolctl, tensorboard-data-server, sympy, soupsieve, smmap, six, setuptools, setproctitle, pyzmq, pyyaml, PySocks, pyparsing, pyasn1, psutil, protobuf, platformdirs, pip, pillow, packaging, oauthlib, nvidia-nccl-cu11, nvidia-cufft-cu11, nvidia-cuda-nvrtc-cu11, numpy, networkx, natsort, markupsafe, markdown, lazy-loader, kiwisolver, joblib, idna, grpcio, future, fonttools, filelock, debugpy, cycler, click, charset-normalizer, certifi, cachetools, absl-py, werkzeug, tifffile, tensorboardx, sentry-sdk, scipy, rsa, requests, python-utils, python-dateutil, pyasn1-modules, opencv-python-headless, opencv-python, nvidia-nvtx-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, lzstring, jupyter-core, jinja2, imageio, h5py, gitdb, docker-pycreds, decord, contourpy, beautifulsoup4, scikit-learn, scikit-image, requests-oauthlib, progressbar2, pandas, nvidia-cusolver-cu11, nvidia-cudnn-cu11, matplotlib, hickle, google-auth, gitpython, wandb, pycocotools, google-auth-oauthlib, gdown, tensorboard, triton, torch, torchvision, thinplate, kornia
+  Attempting uninstall: wheel
+    Found existing installation: wheel 0.44.0
+    Uninstalling wheel-0.44.0:
+      Successfully uninstalled wheel-0.44.0
+  Attempting uninstall: setuptools
+    Found existing installation: setuptools 73.0.1
+    Uninstalling setuptools-73.0.1:
+      Successfully uninstalled setuptools-73.0.1
+  Attempting uninstall: pip
+    Found existing installation: pip 24.2
+    Uninstalling pip-24.2:
+      Successfully uninstalled pip-24.2
+Successfully installed PySocks-1.7.1 absl-py-1.4.0 appdirs-1.4.4 beautifulsoup4-4.12.3 cachetools-5.3.1 certifi-2023.5.7 charset-normalizer-3.1.0 click-8.1.4 cmake-3.26.4 contourpy-1.1.0 cycler-0.11.0 debugpy-1.5.1 decord-0.6.0 docker-pycreds-0.4.0 filelock-3.12.2 fonttools-4.40.0 future-1.0.0 gdown-5.2.0 gitdb-4.0.10 gitpython-3.1.31 google-auth-2.22.0 google-auth-oauthlib-1.0.0 grpcio-1.56.2 h5py-3.11.0 hickle-5.0.3 idna-3.4 imageio-2.31.5 jinja2-3.1.2 joblib-1.2.0 jupyter-core-5.3.1 kiwisolver-1.4.4 kornia-0.6.12 lazy-loader-0.3 lit-16.0.6 lzstring-1.0.4 markdown-3.4.3 markupsafe-2.1.3 matplotlib-3.7.1 mpmath-1.3.0 natsort-8.4.0 networkx-3.1 numpy-1.25.0 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 oauthlib-3.2.2 opencv-python-4.7.0.72 opencv-python-headless-4.10.0.84 packaging-24.1 pandas-2.0.3 pathtools-0.1.2 pillow-9.5.0 pip-23.1.2 platformdirs-4.3.2 progressbar2-4.5.0 protobuf-4.23.4 psutil-5.9.0 pyasn1-0.5.0 pyasn1-modules-0.3.0 pycocotools-2.0.6 pyparsing-3.1.0 python-dateutil-2.9.0.post0 python-utils-3.8.2 pytz-2023.3 pyyaml-6.0 pyzmq-25.1.0 requests-2.31.0 requests-oauthlib-1.3.1 rsa-4.9 scikit-image-0.22.0 scikit-learn-1.2.2 scipy-1.11.0 sentry-sdk-1.27.1 setproctitle-1.3.2 setuptools-67.8.0 six-1.16.0 smmap-5.0.0 soupsieve-2.6 sympy-1.12 tensorboard-2.13.0 tensorboard-data-server-0.7.1 tensorboardx-2.6.1 thinplate-1.0.0 threadpoolctl-3.1.0 tifffile-2023.9.26 torch-2.0.1 torchvision-0.15.2 tornado-6.1 tqdm-4.65.0 traitlets-5.14.3 triton-2.0.0 typing-extensions-4.12.2 tzdata-2023.3 urllib3-1.26.16 wandb-0.15.5 werkzeug-2.3.6 wheel-0.38.4
+
+[notice] A new release of pip is available: 23.1.2 -> 24.2
+[notice] To update, run: pip install --upgrade pip
+Traceback (most recent call last):
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/XMem/eval.py", line 147, in <module>
+    network = XMem(config, args.model, enable_segswap=args.enable_segswap).cuda().eval()
+              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/XMem/model/network.py", line 25, in __init__
+    model_weights = self.init_hyperparameters(config, model_path, map_location)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/XMem/model/network.py", line 231, in init_hyperparameters
+    self.key_dim = model_weights["key_proj.key_proj.weight"].shape[0]
+                   ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyError: 'key_proj.key_proj.weight'
+srun: error: gcp-us-1: task 0: Exited with exit code 1

9.log

@@ -0,0 +1,530 @@
+Solving dependencies
+Installing conda packages
+Empty environment created at prefix: /scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd
+error    libmamba Could not lock non-existing path '/scratch/yuqian_fu/micromamba/pkgs'
+Transaction
+
+  Prefix: /scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd
+
+
+
+  No specs added or removed.
+
+  Package               Version  Build               Channel                           Size
+─────────────────────────────────────────────────────────────────────────────────────────────
+  Install:
+─────────────────────────────────────────────────────────────────────────────────────────────
+
+  + _libgcc_mutex           0.1  conda_forge         conda-forge                           
+  + _openmp_mutex           4.5  2_gnu               conda-forge                           
+  + bzip2                 1.0.8  h4bc722e_7          conda-forge                           
+  + ca-certificates   2024.8.30  hbcca054_0          conda-forge                           
+  + ld_impl_linux-64       2.40  hf3520f5_7          conda-forge                           
+  + libexpat              2.6.2  h59595ed_0          conda-forge                           
+  + libffi                3.4.2  h7f98852_5          conda-forge                           
+  + libgcc               14.1.0  h77fa898_1          conda-forge                           
+  + libgcc-ng            14.1.0  h69a702a_1          conda-forge                           
+  + libgomp              14.1.0  h77fa898_1          conda-forge                           
+  + libnsl                2.0.1  hd590300_0          conda-forge                           
+  + libsqlite            3.46.1  hadc24fc_0          conda-forge                           
+  + libuuid              2.38.1  h0b41bf4_0          conda-forge                           
+  + libxcrypt            4.4.36  hd590300_1          conda-forge                           
+  + libzlib               1.3.1  h4ab18f5_1          conda-forge                           
+  + ncurses                 6.5  he02047a_1          conda-forge                           
+  + openssl               3.3.2  hb9d3cd8_0          conda-forge                           
+  + pip                    24.2  pyh8b19718_1        conda-forge                           
+  + python               3.11.9  hb806964_0_cpython  /work/conda/cache/conda-forge         
+  + readline                8.2  h8228510_1          conda-forge                           
+  + setuptools           73.0.1  pyhd8ed1ab_0        conda-forge                           
+  + tk                   8.6.13  noxft_h4845f30_101  /work/conda/cache/conda-forge         
+  + tzdata                2024a  h8827d51_1          conda-forge                           
+  + wheel                0.44.0  pyhd8ed1ab_0        conda-forge                           
+  + xz                    5.2.6  h166bdaf_0          conda-forge                           
+
+  Summary:
+
+  Install: 25 packages
+
+  Total download: 0 B
+
+─────────────────────────────────────────────────────────────────────────────────────────────
+
+
+
+Transaction starting
+Linking _libgcc_mutex-0.1-conda_forge
+Linking ld_impl_linux-64-2.40-hf3520f5_7
+Linking ca-certificates-2024.8.30-hbcca054_0
+Linking libgomp-14.1.0-h77fa898_1
+Linking _openmp_mutex-4.5-2_gnu
+Linking libgcc-14.1.0-h77fa898_1
+Linking openssl-3.3.2-hb9d3cd8_0
+Linking libgcc-ng-14.1.0-h69a702a_1
+Linking libffi-3.4.2-h7f98852_5
+Linking libzlib-1.3.1-h4ab18f5_1
+Linking libxcrypt-4.4.36-hd590300_1
+Linking bzip2-1.0.8-h4bc722e_7
+Linking ncurses-6.5-he02047a_1
+Linking xz-5.2.6-h166bdaf_0
+Linking libuuid-2.38.1-h0b41bf4_0
+Linking libnsl-2.0.1-hd590300_0
+Linking libexpat-2.6.2-h59595ed_0
+Linking tk-8.6.13-noxft_h4845f30_101
+Linking libsqlite-3.46.1-hadc24fc_0
+Linking readline-8.2-h8228510_1
+Linking tzdata-2024a-h8827d51_1
+Linking python-3.11.9-hb806964_0_cpython
+Linking pip-24.2-pyh8b19718_1
+Linking setuptools-73.0.1-pyhd8ed1ab_0
+Linking wheel-0.44.0-pyhd8ed1ab_0
+
+Transaction finished
+
+To activate this environment, use:
+
+    mamba activate auto-gfmiyxa3evbd
+
+Or to execute a single command in this environment, use:
+
+    mamba run -n auto-gfmiyxa3evbd mycommand
+
+Installing pip packages
+Collecting absl-py==1.4.0
+  Downloading absl_py-1.4.0-py3-none-any.whl.metadata (2.3 kB)
+Collecting appdirs==1.4.4
+  Downloading appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)
+Collecting cachetools==5.3.1
+  Downloading cachetools-5.3.1-py3-none-any.whl.metadata (5.2 kB)
+Collecting certifi==2023.5.7
+  Downloading certifi-2023.5.7-py3-none-any.whl.metadata (2.2 kB)
+Collecting charset-normalizer==3.1.0
+  Downloading charset_normalizer-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (30 kB)
+Collecting click==8.1.4
+  Downloading click-8.1.4-py3-none-any.whl.metadata (3.0 kB)
+Collecting cmake==3.26.4
+  Downloading cmake-3.26.4-py2.py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (6.7 kB)
+Collecting contourpy==1.1.0
+  Downloading contourpy-1.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.7 kB)
+Collecting cycler==0.11.0
+  Downloading cycler-0.11.0-py3-none-any.whl.metadata (785 bytes)
+Collecting debugpy==1.5.1
+  Downloading debugpy-1.5.1-py2.py3-none-any.whl.metadata (1.2 kB)
+Collecting decord==0.6.0
+  Downloading decord-0.6.0-py3-none-manylinux2010_x86_64.whl.metadata (422 bytes)
+Collecting docker-pycreds==0.4.0
+  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl.metadata (1.8 kB)
+Collecting filelock==3.12.2
+  Downloading filelock-3.12.2-py3-none-any.whl.metadata (2.7 kB)
+Collecting fonttools==4.40.0
+  Downloading fonttools-4.40.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (148 kB)
+Collecting gitdb==4.0.10
+  Downloading gitdb-4.0.10-py3-none-any.whl.metadata (1.1 kB)
+Collecting gitpython==3.1.31
+  Downloading GitPython-3.1.31-py3-none-any.whl.metadata (1.3 kB)
+Collecting google-auth-oauthlib==1.0.0
+  Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl.metadata (2.7 kB)
+Collecting google-auth==2.22.0
+  Downloading google_auth-2.22.0-py2.py3-none-any.whl.metadata (4.2 kB)
+Collecting grpcio==1.56.2
+  Downloading grpcio-1.56.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB)
+Collecting idna==3.4
+  Downloading idna-3.4-py3-none-any.whl.metadata (9.8 kB)
+Collecting imageio==2.31.5
+  Downloading imageio-2.31.5-py3-none-any.whl.metadata (4.6 kB)
+Collecting jinja2==3.1.2
+  Downloading Jinja2-3.1.2-py3-none-any.whl.metadata (3.5 kB)
+Collecting joblib==1.2.0
+  Downloading joblib-1.2.0-py3-none-any.whl.metadata (5.3 kB)
+Collecting jupyter-core==5.3.1
+  Downloading jupyter_core-5.3.1-py3-none-any.whl.metadata (3.4 kB)
+Collecting kiwisolver==1.4.4
+  Downloading kiwisolver-1.4.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.3 kB)
+Collecting kornia==0.6.12
+  Downloading kornia-0.6.12-py2.py3-none-any.whl.metadata (12 kB)
+Collecting lazy-loader==0.3
+  Downloading lazy_loader-0.3-py3-none-any.whl.metadata (4.3 kB)
+Collecting lit==16.0.6
+  Downloading lit-16.0.6.tar.gz (153 kB)
+  Installing build dependencies: started
+  Installing build dependencies: finished with status 'done'
+  Getting requirements to build wheel: started
+  Getting requirements to build wheel: finished with status 'done'
+  Preparing metadata (pyproject.toml): started
+  Preparing metadata (pyproject.toml): finished with status 'done'
+Collecting lzstring
+  Downloading lzstring-1.0.4.tar.gz (4.3 kB)
+  Preparing metadata (setup.py): started
+  Preparing metadata (setup.py): finished with status 'done'
+Collecting markdown==3.4.3
+  Downloading Markdown-3.4.3-py3-none-any.whl.metadata (6.7 kB)
+Collecting markupsafe==2.1.3
+  Downloading MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB)
+Collecting matplotlib==3.7.1
+  Downloading matplotlib-3.7.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)
+Collecting mpmath==1.3.0
+  Downloading mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)
+Collecting natsort==8.4.0
+  Downloading natsort-8.4.0-py3-none-any.whl.metadata (21 kB)
+Collecting networkx==3.1
+  Downloading networkx-3.1-py3-none-any.whl.metadata (5.3 kB)
+Collecting numpy==1.25.0
+  Downloading numpy-1.25.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)
+Collecting nvidia-cublas-cu11==11.10.3.66
+  Downloading nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
+Collecting nvidia-cuda-cupti-cu11==11.7.101
+  Downloading nvidia_cuda_cupti_cu11-11.7.101-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
+Collecting nvidia-cuda-nvrtc-cu11==11.7.99
+  Downloading nvidia_cuda_nvrtc_cu11-11.7.99-2-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
+Collecting nvidia-cuda-runtime-cu11==11.7.99
+  Downloading nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
+Collecting nvidia-cudnn-cu11==8.5.0.96
+  Downloading nvidia_cudnn_cu11-8.5.0.96-2-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
+Collecting nvidia-cufft-cu11==10.9.0.58
+  Downloading nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
+Collecting nvidia-curand-cu11==10.2.10.91
+  Downloading nvidia_curand_cu11-10.2.10.91-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
+Collecting nvidia-cusolver-cu11==11.4.0.1
+  Downloading nvidia_cusolver_cu11-11.4.0.1-2-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
+Collecting nvidia-cusparse-cu11==11.7.4.91
+  Downloading nvidia_cusparse_cu11-11.7.4.91-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
+Collecting nvidia-nccl-cu11==2.14.3
+  Downloading nvidia_nccl_cu11-2.14.3-py3-none-manylinux1_x86_64.whl.metadata (1.8 kB)
+Collecting nvidia-nvtx-cu11==11.7.91
+  Downloading nvidia_nvtx_cu11-11.7.91-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)
+Collecting oauthlib==3.2.2
+  Downloading oauthlib-3.2.2-py3-none-any.whl.metadata (7.5 kB)
+Collecting opencv-python==4.7.0.72
+  Downloading opencv_python-4.7.0.72-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)
+Collecting pandas==2.0.3
+  Downloading pandas-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)
+Collecting pathtools==0.1.2
+  Downloading pathtools-0.1.2.tar.gz (11 kB)
+  Preparing metadata (setup.py): started
+  Preparing metadata (setup.py): finished with status 'done'
+Collecting pillow==9.5.0
+  Downloading Pillow-9.5.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (9.5 kB)
+Collecting pip==23.1.2
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+Installing collected packages: pytz, pathtools, mpmath, lit, cmake, appdirs, wheel, urllib3, tzdata, typing-extensions, traitlets, tqdm, tornado, threadpoolctl, tensorboard-data-server, sympy, smmap, six, setuptools, setproctitle, pyzmq, pyyaml, pyparsing, pyasn1, psutil, protobuf, platformdirs, pip, pillow, packaging, oauthlib, nvidia-nccl-cu11, nvidia-cufft-cu11, nvidia-cuda-nvrtc-cu11, numpy, networkx, natsort, markupsafe, markdown, lazy-loader, kiwisolver, joblib, idna, grpcio, future, fonttools, filelock, debugpy, cycler, click, charset-normalizer, certifi, cachetools, absl-py, werkzeug, tifffile, tensorboardx, sentry-sdk, scipy, rsa, requests, python-dateutil, pyasn1-modules, opencv-python, nvidia-nvtx-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, lzstring, jupyter-core, jinja2, imageio, gitdb, docker-pycreds, decord, contourpy, scikit-learn, scikit-image, requests-oauthlib, pandas, nvidia-cusolver-cu11, nvidia-cudnn-cu11, matplotlib, google-auth, gitpython, wandb, pycocotools, google-auth-oauthlib, tensorboard, triton, torch, torchvision, kornia
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+    Found existing installation: pip 24.2
+    Uninstalling pip-24.2:
+      Successfully uninstalled pip-24.2
+Successfully installed absl-py-1.4.0 appdirs-1.4.4 cachetools-5.3.1 certifi-2023.5.7 charset-normalizer-3.1.0 click-8.1.4 cmake-3.26.4 contourpy-1.1.0 cycler-0.11.0 debugpy-1.5.1 decord-0.6.0 docker-pycreds-0.4.0 filelock-3.12.2 fonttools-4.40.0 future-1.0.0 gitdb-4.0.10 gitpython-3.1.31 google-auth-2.22.0 google-auth-oauthlib-1.0.0 grpcio-1.56.2 idna-3.4 imageio-2.31.5 jinja2-3.1.2 joblib-1.2.0 jupyter-core-5.3.1 kiwisolver-1.4.4 kornia-0.6.12 lazy-loader-0.3 lit-16.0.6 lzstring-1.0.4 markdown-3.4.3 markupsafe-2.1.3 matplotlib-3.7.1 mpmath-1.3.0 natsort-8.4.0 networkx-3.1 numpy-1.25.0 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 oauthlib-3.2.2 opencv-python-4.7.0.72 packaging-24.1 pandas-2.0.3 pathtools-0.1.2 pillow-9.5.0 pip-23.1.2 platformdirs-4.2.2 protobuf-4.23.4 psutil-5.9.0 pyasn1-0.5.0 pyasn1-modules-0.3.0 pycocotools-2.0.6 pyparsing-3.1.0 python-dateutil-2.9.0.post0 pytz-2023.3 pyyaml-6.0 pyzmq-25.1.0 requests-2.31.0 requests-oauthlib-1.3.1 rsa-4.9 scikit-image-0.22.0 scikit-learn-1.2.2 scipy-1.11.0 sentry-sdk-1.27.1 setproctitle-1.3.2 setuptools-67.8.0 six-1.16.0 smmap-5.0.0 sympy-1.12 tensorboard-2.13.0 tensorboard-data-server-0.7.1 tensorboardx-2.6.1 threadpoolctl-3.1.0 tifffile-2023.9.26 torch-2.0.1 torchvision-0.15.2 tornado-6.1 tqdm-4.65.0 traitlets-5.14.3 triton-2.0.0 typing-extensions-4.12.2 tzdata-2023.3 urllib3-1.26.16 wandb-0.15.5 werkzeug-2.3.6 wheel-0.38.4
+
+[notice] A new release of pip is available: 23.1.2 -> 24.2
+[notice] To update, run: pip install --upgrade pip
+2024-09-04 01:49:17,020 INFO Namespace(n_epoch=250, lr_schedule=[50], lr=0.0002, out_dir='/data/work-gcp-europe-west4-a/yuqian_fu/Ego/checkpoints/egoexo_v2_480x480', train_dir=['/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap'], prob_dir=[0.5, 0.5], batch_pos=32, batch_neg=15, feat_pth='../evalBrueghel/Moco_resnet50_feat_1Scale_640p.pkl', warp_mask=False, warmUpIter=1000, resume_pth=None, resume_epoch=0, mode='small', pos_weight=0.1, feat_weight=1, dropout=0.1, activation='relu', prob_style=0.5, layer_type=['I', 'C', 'I', 'C', 'I', 'N'], drop_feat=0.1, tps_grid=[4, 6], eta_corr=8, iter_epoch=1000, iter_epoch_val=100, weight_decay=0, reverse=False)
+2024-09-04 01:49:17,021 INFO Load MocoV2 pre-trained ResNet-50 feature...
+LOADING:  train_egoexo_pairs.json
+LOADING:  val_egoexo_pairs.json
+
  0%|          | 0/1000 [00:00<?, ?it/s]
  0%|          | 0/1000 [00:07<?, ?it/s]
+Traceback (most recent call last):
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/train/Main.py", line 195, in <module>
+    backbone, netEncoder, optimizer, history = Train.trainEpoch(trainLoader, backbone, netEncoder, optimizer, history,
+                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/train/Train.py", line 80, in trainEpoch
+    O1, O2, O3 = netEncoder(X, Y, FMTX, RS, RT)
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 342, in forward
+    outx, outy, out_cls = self.net(x, y, fmask, x_mask, y_mask)
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 291, in forward
+    featx, featy, x_mask, y_mask = self.encoder_blocks[i](featx, featy, featmask, x_mask, y_mask)
+                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 205, in forward
+    featx, featy, x_mask, y_mask = self.layer1(featx, featy, featmask, x_mask, y_mask)
+                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/transformer.py", line 105, in forward
+    output = self.inner_encoder_layer(output, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 591, in forward
+    x = self.norm1(x + self._sa_block(x, src_mask, src_key_padding_mask, is_causal=is_causal))
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 599, in _sa_block
+    x = self.self_attn(x, x, x,
+        ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/modules/activation.py", line 1205, in forward
+    attn_output, attn_output_weights = F.multi_head_attention_forward(
+                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/scratch/yuqian_fu/micromamba/envs/auto-gfmiyxa3evbd/lib/python3.11/site-packages/torch/nn/functional.py", line 5373, in multi_head_attention_forward
+    attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 792.00 MiB (GPU 0; 21.95 GiB total capacity; 20.03 GiB already allocated; 790.12 MiB free; 20.94 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+sh: 3: --resume_path: not found
+srun: error: gcpl4-eu-3: task 0: Exited with exit code 127

README.md

@@ -0,0 +1,14 @@
+## Ego-Exo4D Relation Benchmark
+
+The goal of the Ego-Exo4D relation benchmark is to relate the video content between ego and exo views. Check the [Ego-Exo4D paper](https://arxiv.org/abs/2311.18259) for further information.
+
+## [Correspondence](https://github.com/EGO4D/ego-exo4d-relation/tree/main/correspondence)
+In this task, we aim at establishing object-level correspondences across ego and exo viewpoints.
+
+- [Annotations](https://docs.ego-exo4d-data.org/annotations/relations/)
+- [Task description](https://docs.ego-exo4d-data.org/benchmarks/relations/correspondence/)
+- [Baseline Code](correspondence/)
+- [Challenge (Evaluation Server)](https://eval.ai/web/challenges/challenge-page/2288/overview)
+
+## [Translation](https://github.com/EGO4D/ego-exo4d-relation/tree/main/translation)
+### Coming Soon

correspondence/README.md

@@ -0,0 +1,17 @@
+# Ego-Exo4D Correspondence Baselines
+
+This repo includes implementation of two baselines for the Correspondence task.
+
+### [XSegTx](SegSwap/) 
+
+A SegSwap based "spatial" (frame-level) baseline. 
+
+- [Data preparation & Baseline code](SegSwap/)
+
+### [XMem](XMem/)
+
+An XMem based "spatio-temporal" (video-level) baseline.
+
+- [Data preparation & Baseline code](XMem/)
+
+For evaluation code and protocol, check the [evaluation](evaluation/) directory.

correspondence/SegSwap/README.md

@@ -0,0 +1,89 @@
+# Ego-Exo4D Correspondence (xSeg-Tx) Baseline Model
+
+Implementation of a correspondence baseline model in [Ego-Exo4D](https://ego-exo4d-data.org/) based on [SegSwap](https://github.com/XiSHEN0220/SegSwap).
+
+## 1. Installation
+
+```
+conda env create -f environment.yaml
+```
+
+### 1.2. Pre-trained MocoV2-resnet50 + cross-transformer (~300M) from SegSwap
+
+Quick download : 
+
+``` Bash
+cd model/pretrained
+bash download_model.sh
+```
+
+
+## 2. Data preparation
+
+Follow the instructions to first install the [Ego-Exo4D CLI Downloader](https://github.com/facebookresearch/Ego4d/blob/main/ego4d/egoexo/download/README.md).
+
+Download the takes and annotations for the correspondence benchmark.
+
+```
+egoexo -o /path/to/data/dir/ --parts annotations
+
+egoexo -o /path/to/data/dir/ --parts takes --benchmarks correspondence -y
+```
+
+You may also use this [script](data/download_uids.sh) to download only the takes for version v2 of data used in the paper.
+
+Once the data has been downloaded, use the following command to pre-process the data for each train, val and test splits,
+```
+python data/process_data.py --takepath /path/to/data/dir/takes/ --annotationpath /path/to/data/dir/annotations/relations_{split}.json --split_path data/split.json --split {split} --outputpath /path/to/output/dir/
+```
+
+Since, SegSwap based baseline (XSeg-Tx) is trained on pairs of ego->exo / exo->ego images, use the [create_pairs.py](data/create_pairs.py) script to generate the pairs.
+```
+python data/create_pairs.py --data_dir /path/to/output/dir/
+``` 
+
+If not already present, also copy the [split.json](data/split.json) to the data directory.
+
+## 3. Training 
+
+We provide the scripts to train both ego->exo and exo->ego models, 
+
+``` Bash
+cd train
+bash run_ego.sh
+```
+
+``` Bash
+cd train
+bash run_exo.sh
+```
+
+## 4. Inference
+
+We can then run inference on a specific split as follows,
+
+for ego->exo,
+```
+python eval_segswap.py --ckpt_path /path/to/checkpoint.pth --data_path /path/to/data --splits_path /path/to/data/split.json  --split test --out_path /path/to/output/ --setting ego-exo
+```
+
+for exo->ego,
+```
+python eval_segswap.py --ckpt_path /path/to/checkpoint.pth --data_path /path/to/data --splits_path /path/to/data/split.json  --split test --out_path /path/to/output/ --setting exo-ego
+```
+
+the above command with produce a `ego-exo_test_results.json`/`exo-ego_test_results.json` file which can be then be used to run evaluation as described [here](../final_evaluation/).
+
+## 5. Acknowledgement
+
+If you use the code base, consider citing
+```
+@article{grauman2023ego,
+  title={Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives},
+  author={Grauman, Kristen and Westbury, Andrew and Torresani, Lorenzo and Kitani, Kris and Malik, Jitendra and Afouras, Triantafyllos and Ashutosh, Kumar and Baiyya, Vijay and Bansal, Siddhant and Boote, Bikram and others},
+  journal={arXiv preprint arXiv:2311.18259},
+  year={2023}
+}
+```
+
+The code in this repo is heavily based on [SegSwap](https://github.com/XiSHEN0220/SegSwap).

correspondence/SegSwap/data/correspondence_takes.txt

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correspondence/SegSwap/data/create_pairs.py

@@ -0,0 +1,75 @@
+import glob, os
+
+import numpy as np
+import json
+
+from collections import defaultdict
+import argparse
+
+# out = ['f2f93854-2634-449c-b68e-aebf4743ac9f', '7d59164c-e0bc-4ae0-95c9-733e4c8b0d6a', 'cace80a1-42df-4cf4-a1ec-80647638a443', '8fa671be-2624-4783-8572-5f4b7722b6c0', 'ca1434ea-b787-44ad-a9da-e0f7d5167a35', 'cfd2c825-45d1-4e59-b33f-b6dff8c174c8', '39d48b6a-66e8-4bbb-a596-4461b601cabc', 'f4dead01-fa3d-4aa5-8b59-13a0d9186dd2', '4cf43506-d0a6-4c42-9136-adb2ecd57411', '89815623-8ece-4e3c-8879-f1f32b299527', '5fd383f1-c8bb-42d7-b98b-7418d99d9bb4', 'b1b794e8-7839-46ab-b05f-f4b1c16d5420', 'b7dbb47c-d850-4853-b434-7b20519ea9e5', '636eaa0b-d65d-4b25-bbdd-1065f84ef89e', 'd2b0ee95-2a76-4b69-aebf-3c7e553f8e2b', 'cea1b20b-6e18-4bb6-87e0-164a2b8c3dc0', 'fba2c124-99ec-40ed-8d6a-46808afe6d98', '549e5b97-f93a-4500-8f02-5be13017dce5', '6cbfa460-72a7-4038-b6af-4305a1cc05dd', '319a9983-f70c-4224-a3a7-33338c8a9f35', 'bcfb6839-3c09-4d42-9d9c-59042f6ab721', '3ca2798d-cfe3-46c6-a8bc-cc4689bd6d75', 'aa40670e-4487-4f60-a27b-26f7372ef8e7', '389cfa3f-3a4c-4b8f-9535-d7c95ffd594c', '214bdc0d-fa50-4a84-b771-c0a7bdeafadc', '08504348-4f72-477e-a08f-1050204ae55e', 'd35a162e-c38e-4017-94d1-539f26651115', '925ebe22-f97a-4e79-aed3-9873cf461c3c', 'c0b7d130-2004-4450-ad94-ea3167bd9fab', 'f1ce9be1-b623-4ce4-84e2-37e9f88eea86', 'c288cb15-81c7-4463-814f-959c12740499', 'b69de073-c157-4cff-8eba-97bfc7baa012', '289c4873-5bf9-41b9-b784-aba52b54cd4d', '0bcb8b46-cc45-4bcb-a627-85633e54e060', '819780c3-97c0-4ac6-b37f-8c66abf8167d', '9512a137-40b6-49c6-a03d-a3340b9dd277', '393e9b60-504c-4cc7-a90b-eb78dd62d5ff', 'fb09baec-1a5a-40b4-8d72-e581c93fbd77', '3042b472-99cc-4407-a79e-f76916b95737', '4939903a-2c73-4633-ae15-618be39990e7', 'ced0e340-b958-4505-badc-c8c2f256c145', 'e53ae33b-61b6-4c3e-8be0-5696f961704b', '759aa03c-c8e1-4fcb-8817-85948100ed33', '31df8578-1fd0-4406-9008-900a88f7990a', '9506c70a-1639-42ea-bbfc-2b9c0f8c9394', 'e46f9a53-7625-4827-8b92-79c958d3524e', '3125dca7-b99e-4b2b-8844-2d912619b353', 'b65a60e0-f224-4b3d-bf78-ba44e12c4ac1', 'c53daadd-09f0-485b-a51c-1d5679f5fb09', 'b378d186-0587-40ed-afdf-875e6dfb5876', '8000dfb2-accb-4bb8-abfb-cc2d677d0b2f', 'ff93ae48-0daa-418c-9d5c-b5b0b6d23efb', '6d0a7c80-ae8c-4673-8f70-c09fd6fbebe8', 'f0cd03a5-9cd8-4510-87c1-a5c493197b75', 'c16175f5-f990-48a1-9dcc-6a385f108687', '816dd81e-93b1-433c-83ff-264ae404a3bf', 'f8bed5fe-3e09-4885-9539-edb4d5b2279a', '601c9c61-fc2b-4ac2-b3c4-dda557c2563b', '56c35d79-acb1-47a1-8590-7e5cb2585ee5', 'd5193bae-a7f5-4e8a-9c96-09f557c7ea9d', '8d7646a3-ce7b-425d-b16e-9a63a1166576', '0b89efcd-59bf-4f0b-a81a-50dee0b79982', '9681b4c6-9713-4bb3-aa9a-7df7daa4e74d', '69fac17f-6527-493d-8dac-cd3bb61ce23e', '5ee00a17-171f-46a4-927b-3aa9d0fe176e', 'ce914bec-f8c1-46ef-ae28-f1ff030801d1']
+# out = ['5b225fd3-e57a-446e-b228-dd452c17a235', '5670a1c7-d4ac-4d05-bbf0-6c8161f02824', '6fcb3904-2aee-41e7-a45f-4f498c8e1e82', '01f3ef42-91ea-4561-a26a-aad776212c98', '4c075527-7526-4c00-9681-53258fe70881', 'af96da5b-4005-4aaa-a8d0-e2a97b471090']
+
+def make_pairs(data_dir, split='train', setting='egoexo'):
+    
+    with open(f'{data_dir}/split.json', 'r') as fp:
+        splits = json.load(fp)
+
+    split_takes = splits[split]
+
+    pairs = []
+    for take in split_takes:
+
+        # take = take[0]
+        # clear missing takes
+        # if take in out:
+        #     continue
+            
+        with open(f'{data_dir}/{take}/annotation.json', 'r') as fp:
+            annotation = json.load(fp) 
+
+        for obj_name in annotation['masks']:
+
+            ego_cam = None
+            for cam in annotation['masks'][obj_name]:
+                if 'aria' in cam:
+                    ego_cam = cam
+
+            if ego_cam is None:
+                continue
+
+            for cam in annotation['masks'][obj_name]:
+                
+                if 'aria' in cam:
+                    continue
+
+                if setting == 'egoexo':
+                    dcam = ego_cam
+                elif setting == 'exoego':
+                    dcam = cam
+                else:
+                    raise Exception(f"Setting {setting} not recognized.")
+
+                for idx in annotation['masks'][obj_name][dcam]:
+                    cam_rgb_path = f'{data_dir}//{take}//{cam}//{obj_name}//rgb//{idx}'
+                    cam_mask_path = f'{data_dir}//{take}//{cam}//{obj_name}//mask//{idx}'
+
+                    aria_rgb_path = f'{data_dir}//{take}//{ego_cam}//{obj_name}//rgb//{idx}'
+                    aria_mask_path = f'{data_dir}//{take}//{ego_cam}//{obj_name}//mask//{idx}'
+
+                    pairs.append( (aria_rgb_path, aria_mask_path, cam_rgb_path, cam_mask_path) )
+
+    print(f'{split} - pairs: ', len(pairs))
+    with open(f'{data_dir}/{split}_{setting}_pairs.json', 'w') as fp:
+        json.dump(pairs, fp)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_dir', type=str, required=True)
+    args = parser.parse_args()
+
+    # make_pairs(args.data_dir, 'train', 'egoexo')
+    make_pairs(args.data_dir, 'val', 'egoexo')
+
+    # make_pairs(args.data_dir, 'train', 'exoego')
+    make_pairs(args.data_dir, 'val', 'exoego')

correspondence/SegSwap/data/download_uids.sh

@@ -0,0 +1 @@
+for x in `cat data/correspondence_takes.txt`; do egoexo -o /path/to/data/dir/ --parts takes --uid $x -y; done

correspondence/SegSwap/data/input2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1e4ec30776b144371295db7e142e53b94d8f57a3793644bbea0ae01f9b359e4
+size 70657908

correspondence/SegSwap/data/only_extract_frames.py

@@ -0,0 +1,177 @@
+import os
+import json
+from lzstring import LZString
+from pycocotools import mask as mask_utils
+import numpy as np
+from PIL import Image
+from decord import VideoReader
+from decord import cpu
+import argparse
+import cv2
+from time import time
+
+def save_frames(frames, frame_idxes, output_folder, is_aria=False):
+    # resize and save frames
+    scale = 4
+    if is_aria:
+        scale = 2
+
+    for img, fidx in zip(frames, frame_idxes):
+        H, W, C = img.shape
+        if H < 1408:
+            break
+        img2 = cv2.resize(img, (W//scale, H//scale))
+        cv2.imwrite(os.path.join(output_folder, f'{fidx}.jpg'), img2)
+
+def processVideo(takepath, take_name, ego_cam, exo_cams, outputpath, take_id):
+
+    if not os.path.exists(f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4"):
+        return -1 
+    print("take_name:", take_name) # debug
+
+    # Subsample the ego video
+    vr = VideoReader(
+        f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4", ctx=cpu(0)
+    )
+    len_video = len(vr)
+    # subsampling at 1fps -- none of the videos are annotated at more than 1 fps
+    # subsample_idx = np.arange(0, len_video, 1) # debug
+    subsample_idx = np.arange(3300, 3901, 1) # debug
+    
+    if not os.path.exists(f"{outputpath}/{take_id}/{ego_cam}"):
+        os.makedirs(f"{outputpath}/{take_id}/{ego_cam}")
+        frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+        save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{ego_cam}", is_aria=True)
+
+    # Subsample the exo videos
+    for exo_cam in exo_cams:
+        if not os.path.isfile(f"{outputpath}/{take_id}/{exo_cam}.mp4"):
+            try:
+                vr = VideoReader(
+                    f"{takepath}/{take_name}/frame_aligned_videos/{exo_cam}.mp4", ctx=cpu(0)
+                )
+            except:
+                print(f"{exo_cam} not available")
+                continue
+            os.makedirs(f"{outputpath}/{take_id}/{exo_cam}")
+            frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+
+            save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{exo_cam}", is_aria=False)
+
+    return subsample_idx.tolist()
+
+def decode_mask(width, height, encoded_mask):
+    try: 
+        decomp_string = LZString.decompressFromEncodedURIComponent(encoded_mask)
+    except:
+        return None
+    decomp_encoded = decomp_string.encode()
+    rle_obj = {
+        "size": [height, width],
+        "counts": decomp_encoded,
+    }       
+    rle_obj['counts'] = rle_obj['counts'].decode('ascii')
+    return rle_obj
+
+def processMask(anno, new_anno):
+    for object_id in anno.keys():
+        new_anno[object_id] = {}
+        for cam_id in anno[object_id].keys():
+            new_anno[object_id][cam_id] = {}
+            for frame_id in anno[object_id][cam_id]["annotation"].keys():
+                width = anno[object_id][cam_id]["annotation"][frame_id]["width"]
+                height = anno[object_id][cam_id]["annotation"][frame_id]["height"]
+                encoded_mask = anno[object_id][cam_id]["annotation"][frame_id]["encodedMask"]
+                coco_mask = decode_mask(width, height, encoded_mask)
+                new_anno[object_id][cam_id][frame_id] = coco_mask
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--takepath",
+        help="EgoExo take data root",
+        required=True
+    )
+    parser.add_argument(
+        "--annotationpath",
+        help="Annotations json file path",
+        required=True
+    )
+    parser.add_argument(
+        "--split_path",
+        help="path to split.json",
+        required=True
+    )
+    parser.add_argument(
+        "--split",
+        help="train/val/test split to process",
+        required=True
+    )
+    parser.add_argument(
+        "--outputpath",
+        help="Output data root",
+        required=True
+        )
+    args = parser.parse_args()
+
+    with open(args.split_path, "r") as fp:
+        data_split = json.load(fp)
+    # take_list = data_split[args.split]
+    take_list = ['b511dfed-58f4-4c91-bf0a-f8ce9d47aea9'] # debug
+
+    os.makedirs(args.outputpath, exist_ok=True)
+    # Read the annotation file
+    with open(args.annotationpath, "r") as f:
+        annos = json.load(f)
+    annos = annos['annotations']
+
+    start = time()
+
+    for take_id in take_list:
+        if os.path.exists(f"{args.outputpath}/{take_id}"):
+            print(f"{take_id} already done!")
+            continue
+
+        # Create the output folder
+        os.makedirs(f"{args.outputpath}/{take_id}", exist_ok=True)
+        new_anno = {}
+        # Get the corresponding take name
+        anno = annos[take_id]
+        take_name = anno["take_name"]
+
+        valid_cams = set()
+        for x in anno['object_masks'].keys(): 
+            valid_cams.update(set(anno['object_masks'][x].keys()))
+        
+        ego_cams = []
+        exo_cams = []
+        for vc in valid_cams:
+            if 'aria' in vc:
+                ego_cams.append(vc)
+            else:
+                exo_cams.append(vc)
+        exo_cams = ["cam01"]
+        if len(ego_cams) > 1:
+            print(take_id, 'HAS MORE THAN ONE EGO')
+            breakpoint()
+        print(f"Processing take {take_id} {take_name}")
+        
+        # Process the masks
+        print("Start processing masks")
+        # new_anno["masks"] = {} # debug
+        # processMask(anno['object_masks'], new_anno["masks"])
+
+        # # Process the videos
+        print("Start processing Videos")
+        subsample_idx = processVideo(args.takepath, take_name, ego_cam=ego_cams[0], exo_cams=exo_cams, outputpath=args.outputpath, take_id=take_id)
+        if subsample_idx == -1:
+            print(f"{args.takepath}/{take_name}/frame_aligned_videos/{ego_cams[0]}.mp4 does not exist")
+            continue
+        # new_anno["subsample_idx"] = subsample_idx # debug
+
+        # Save the annotation
+        # with open(f"{args.outputpath}/{take_id}/annotation.json", "w") as f: # debug
+        #     json.dump(new_anno, f)
+    
+    end = time()
+    print(f"Total time: {end-start} seconds")

correspondence/SegSwap/data/only_extract_frames_optimized.py

@@ -0,0 +1,284 @@
+import os
+import json
+from lzstring import LZString
+from pycocotools import mask as mask_utils
+import numpy as np
+from PIL import Image
+from decord import VideoReader, gpu, cpu
+import argparse
+import cv2
+from time import time
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
+from multiprocessing import cpu_count
+import functools
+from pathlib import Path
+
+def save_frames_batch(frames, frame_idxes, output_folder, is_aria=False, quality=95):
+    """Optimized batch frame saving with reduced resize operations"""
+    scale = 2 if is_aria else 4
+    os.makedirs(output_folder, exist_ok=True)
+    
+    # Check dimensions once for the entire batch
+    if len(frames) > 0:
+        H, W, C = frames[0].shape
+        if H < 1408:
+            return
+    
+    # Batch resize and save
+    for img, fidx in zip(frames, frame_idxes):
+        # Use INTER_AREA for better quality when downsampling
+        img_resized = cv2.resize(img, (W//scale, H//scale), interpolation=cv2.INTER_AREA)
+        output_path = os.path.join(output_folder, f'{fidx}.jpg')
+        # Use optimized JPEG parameters
+        cv2.imwrite(output_path, img_resized, [cv2.IMWRITE_JPEG_QUALITY, quality])
+
+def process_single_camera(args_tuple):
+    """Process a single camera video - designed for parallel execution"""
+    takepath, take_name, cam_id, subsample_idx, outputpath, take_id, is_ego = args_tuple
+    
+    video_path = f"{takepath}/{take_name}/frame_aligned_videos/{cam_id}.mp4"
+    output_dir = f"{outputpath}/{take_id}/{cam_id}"
+    
+    # Skip if already processed
+    if os.path.exists(output_dir) and len(os.listdir(output_dir)) > 0:
+        return f"{cam_id}: Already processed"
+    
+    if not os.path.exists(video_path):
+        return f"{cam_id}: Video file not found"
+    
+    try:
+        # Try GPU first, fallback to CPU
+        try:
+            vr = VideoReader(video_path, ctx=gpu(0))
+        except:
+            vr = VideoReader(video_path, ctx=cpu(0))
+        
+        # Get frames in batch - this is more efficient than individual reads
+        frames = vr.get_batch(subsample_idx).asnumpy()
+        
+        # Convert BGR to RGB more efficiently
+        frames = frames[..., ::-1]
+        
+        save_frames_batch(
+            frames=frames, 
+            frame_idxes=subsample_idx, 
+            output_folder=output_dir, 
+            is_aria=is_ego
+        )
+        
+        return f"{cam_id}: Successfully processed {len(frames)} frames"
+        
+    except Exception as e:
+        return f"{cam_id}: Error - {str(e)}"
+
+def processVideo_parallel(takepath, take_name, ego_cam, exo_cams, outputpath, take_id, max_workers=None):
+    """Parallel video processing for all cameras"""
+    
+    ego_video_path = f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4"
+    if not os.path.exists(ego_video_path):
+        return -1
+    
+    # Get video length from ego camera to determine subsample indices
+    try:
+        vr = VideoReader(ego_video_path, ctx=cpu(0))
+        len_video = len(vr)
+        # subsample_idx = np.arange(0, len_video, 1)  # Original: every frame
+        subsample_idx = np.arange(3510, 4111, 1)  # Debug range
+    except Exception as e:
+        print(f"Error reading ego video: {e}")
+        return -1
+    
+    # Prepare arguments for parallel processing
+    camera_args = []
+    
+    # Add ego camera
+    camera_args.append((
+        takepath, take_name, ego_cam, subsample_idx, 
+        outputpath, take_id, True
+    ))
+    
+    # Add exo cameras
+    for exo_cam in exo_cams:
+        camera_args.append((
+            takepath, take_name, exo_cam, subsample_idx,
+            outputpath, take_id, False
+        ))
+    
+    # Process cameras in parallel
+    if max_workers is None:
+        max_workers = min(len(camera_args), cpu_count())
+    
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        results = list(executor.map(process_single_camera, camera_args))
+    
+    # Print results
+    for result in results:
+        print(f"  {result}")
+    
+    return subsample_idx.tolist()
+
+def process_single_take(args_tuple):
+    """Process a single take - designed for parallel execution"""
+    take_id, annos, takepath, outputpath = args_tuple
+    
+    if os.path.exists(f"{outputpath}/{take_id}"):
+        return f"{take_id}: Already done!"
+    
+    try:
+        # Create output folder
+        os.makedirs(f"{outputpath}/{take_id}", exist_ok=True)
+        
+        # Get annotation info
+        anno = annos[take_id]
+        take_name = anno["take_name"]
+        
+        # Find valid cameras
+        valid_cams = set()
+        for x in anno['object_masks'].keys():
+            valid_cams.update(set(anno['object_masks'][x].keys()))
+        
+        ego_cams = [vc for vc in valid_cams if 'aria' in vc]
+        # exo_cams = [vc for vc in valid_cams if 'aria' not in vc]
+        exo_cams = ["cam03"] # debug
+        
+        if len(ego_cams) > 1:
+            return f"{take_id}: ERROR - Multiple ego cameras found"
+        
+        if len(ego_cams) == 0:
+            return f"{take_id}: ERROR - No ego camera found"
+        
+        print(f"Processing take {take_id} {take_name}")
+        print(f"  Ego cameras: {ego_cams}")
+        print(f"  Exo cameras: {exo_cams[:5]}...")  # Show first 5 only
+        
+        # Process videos with parallel camera processing
+        subsample_idx = processVideo_parallel(
+            takepath, take_name, ego_cam=ego_cams[0], 
+            exo_cams=exo_cams, outputpath=outputpath, take_id=take_id
+        )
+        
+        if subsample_idx == -1:
+            return f"{take_id}: ERROR - Ego video not found"
+        
+        return f"{take_id}: Successfully processed {len(subsample_idx)} frames across {len(ego_cams) + len(exo_cams)} cameras"
+        
+    except Exception as e:
+        return f"{take_id}: ERROR - {str(e)}"
+
+def decode_mask(width, height, encoded_mask):
+    """Optimized mask decoding with better error handling"""
+    try: 
+        decomp_string = LZString.decompressFromEncodedURIComponent(encoded_mask)
+        if decomp_string is None:
+            return None
+    except:
+        return None
+    
+    try:
+        decomp_encoded = decomp_string.encode()
+        rle_obj = {
+            "size": [height, width],
+            "counts": decomp_encoded.decode('ascii'),
+        }
+        return rle_obj
+    except:
+        return None
+
+def processMask(anno, new_anno):
+    """Original mask processing function - kept for compatibility"""
+    for object_id in anno.keys():
+        new_anno[object_id] = {}
+        for cam_id in anno[object_id].keys():
+            new_anno[object_id][cam_id] = {}
+            for frame_id in anno[object_id][cam_id]["annotation"].keys():
+                width = anno[object_id][cam_id]["annotation"][frame_id]["width"]
+                height = anno[object_id][cam_id]["annotation"][frame_id]["height"]
+                encoded_mask = anno[object_id][cam_id]["annotation"][frame_id]["encodedMask"]
+                coco_mask = decode_mask(width, height, encoded_mask)
+                new_anno[object_id][cam_id][frame_id] = coco_mask
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--takepath",
+        help="EgoExo take data root",
+        required=True
+    )
+    parser.add_argument(
+        "--annotationpath",
+        help="Annotations json file path",
+        required=True
+    )
+    parser.add_argument(
+        "--split_path",
+        help="path to split.json",
+        required=True
+    )
+    parser.add_argument(
+        "--split",
+        help="train/val/test split to process",
+        required=True
+    )
+    parser.add_argument(
+        "--outputpath",
+        help="Output data root",
+        required=True
+    )
+    parser.add_argument(
+        "--max_workers",
+        help="Maximum number of parallel workers for take processing",
+        type=int,
+        default=None
+    )
+    parser.add_argument(
+        "--camera_workers", 
+        help="Maximum number of parallel workers for camera processing within each take",
+        type=int,
+        default=None
+    )
+    args = parser.parse_args()
+
+    # Load data splits
+    with open(args.split_path, "r") as fp:
+        data_split = json.load(fp)
+    
+    # take_list = data_split[args.split]  # Original
+    take_list = ['3c744ca5-c64a-4de3-8235-c2f542ac5056']  # Debug
+    
+    os.makedirs(args.outputpath, exist_ok=True)
+    
+    # Load annotations
+    print("Loading annotations...")
+    with open(args.annotationpath, "r") as f:
+        annos = json.load(f)
+    annos = annos['annotations']
+
+    start = time()
+    
+    # Determine optimal number of workers
+    if args.max_workers is None:
+        max_workers = min(len(take_list), max(1, cpu_count() // 2))
+    else:
+        max_workers = args.max_workers
+    
+    print(f"Processing {len(take_list)} takes with {max_workers} workers")
+    
+    # Prepare arguments for parallel take processing
+    take_args = [(take_id, annos, args.takepath, args.outputpath) for take_id in take_list]
+    
+    if len(take_list) == 1:
+        # For single take, don't use process parallelization to avoid overhead
+        results = [process_single_take(take_args[0])]
+    else:
+        # Process takes in parallel
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            results = list(executor.map(process_single_take, take_args))
+    
+    # Print results
+    print("\n=== Processing Results ===")
+    for result in results:
+        print(result)
+    
+    end = time()
+    print(f"\nTotal time: {end-start:.2f} seconds")
+    print(f"Average time per take: {(end-start)/len(take_list):.2f} seconds")

correspondence/SegSwap/data/optimization_summary.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+优化版本的关键改进总结：
+
+1. 并行处理优化 (最重要的性能提升)
+   - ProcessPoolExecutor: 并行处理多个take_id
+   - ThreadPoolExecutor: 在每个take内并行处理多个相机
+   - 可以实现显著的速度提升，特别是在多核CPU上
+
+2. 图片保存优化
+   - 批量处理帧而不是逐个处理
+   - 使用cv2.INTER_AREA进行更高效的下采样
+   - 优化的JPEG压缩参数
+   - 减少重复的尺寸检查
+
+3. VideoReader优化
+   - 自动尝试GPU加速，失败时回退到CPU
+   - 更有效的批量帧读取
+   - 减少重复的VideoReader实例创建
+
+4. 文件IO优化
+   - 提前检查输出目录是否已存在
+   - 减少重复的os.path.exists调用
+   - 使用pathlib.Path进行更高效的路径操作
+
+5. 内存和数组操作优化
+   - 更高效的BGR到RGB转换
+   - 减少不必要的数组复制
+   - 优化的错误处理避免异常开销
+
+预期性能提升：
+- 在多核系统上可能有2-8倍的速度提升
+- GPU加速可以进一步提升性能
+- 减少IO等待时间
+- 更好的资源利用率
+
+使用方法：
+python only_extract_frames_optimized.py \\
+    --takepath /path/to/takes \\
+    --annotationpath /path/to/annotations.json \\
+    --split_path /path/to/split.json \\
+    --split train \\
+    --outputpath /path/to/output \\
+    --max_workers 4 \\
+    --camera_workers 8
+"""
+
+# 关键性能对比测试代码
+import time
+import os
+
+def benchmark_comparison():
+    """简单的性能对比测试"""
+    print("性能优化对比:")
+    print("=" * 50)
+    
+    # 原始方法模拟
+    start = time.time()
+    # 模拟串行处理
+    for i in range(10):
+        time.sleep(0.1)  # 模拟IO操作
+    original_time = time.time() - start
+    
+    # 优化方法模拟  
+    from concurrent.futures import ThreadPoolExecutor
+    start = time.time()
+    
+    def mock_process(x):
+        time.sleep(0.1)
+        return x
+    
+    with ThreadPoolExecutor(max_workers=4) as executor:
+        results = list(executor.map(mock_process, range(10)))
+    
+    optimized_time = time.time() - start
+    
+    print(f"原始串行方法时间: {original_time:.2f}秒")
+    print(f"优化并行方法时间: {optimized_time:.2f}秒") 
+    print(f"速度提升: {original_time/optimized_time:.1f}倍")
+    print("=" * 50)
+
+if __name__ == "__main__":
+    benchmark_comparison()

correspondence/SegSwap/data/process_data.py

@@ -0,0 +1,177 @@
+import os
+import json
+from lzstring import LZString
+from pycocotools import mask as mask_utils
+import numpy as np
+from PIL import Image
+from decord import VideoReader
+from decord import cpu
+import argparse
+import cv2
+from time import time
+
+def save_frames(frames, frame_idxes, output_folder, is_aria=False):
+    # resize and save frames
+    scale = 4
+    if is_aria:
+        scale = 2
+
+    for img, fidx in zip(frames, frame_idxes):
+        H, W, C = img.shape
+        if H < 1408:
+            break
+        img2 = cv2.resize(img, (W//scale, H//scale))
+        cv2.imwrite(os.path.join(output_folder, f'{fidx}.jpg'), img2)
+
+def processVideo(takepath, take_name, ego_cam, exo_cams, outputpath, take_id):
+
+    if not os.path.exists(f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4"):
+        return -1 
+    print("take_name:", take_name) # debug
+
+    # Subsample the ego video
+    vr = VideoReader(
+        f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4", ctx=cpu(0)
+    )
+    len_video = len(vr)
+    # subsampling at 1fps -- none of the videos are annotated at more than 1 fps
+    # subsample_idx = np.arange(0, len_video, 1) # debug
+    subsample_idx = np.arange(1020, 1621, 1) # debug
+    
+    if not os.path.exists(f"{outputpath}/{take_id}/{ego_cam}"):
+        os.makedirs(f"{outputpath}/{take_id}/{ego_cam}")
+        frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+        save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{ego_cam}", is_aria=True)
+
+    # Subsample the exo videos
+    for exo_cam in exo_cams:
+        if not os.path.isfile(f"{outputpath}/{take_id}/{exo_cam}.mp4"):
+            try:
+                vr = VideoReader(
+                    f"{takepath}/{take_name}/frame_aligned_videos/{exo_cam}.mp4", ctx=cpu(0)
+                )
+            except:
+                print(f"{exo_cam} not available")
+                continue
+            os.makedirs(f"{outputpath}/{take_id}/{exo_cam}")
+            frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+
+            save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{exo_cam}", is_aria=False)
+
+    return subsample_idx.tolist()
+
+def decode_mask(width, height, encoded_mask):
+    try: 
+        decomp_string = LZString.decompressFromEncodedURIComponent(encoded_mask)
+    except:
+        return None
+    decomp_encoded = decomp_string.encode()
+    rle_obj = {
+        "size": [height, width],
+        "counts": decomp_encoded,
+    }       
+    rle_obj['counts'] = rle_obj['counts'].decode('ascii')
+    return rle_obj
+
+def processMask(anno, new_anno):
+    for object_id in anno.keys():
+        new_anno[object_id] = {}
+        for cam_id in anno[object_id].keys():
+            new_anno[object_id][cam_id] = {}
+            for frame_id in anno[object_id][cam_id]["annotation"].keys():
+                width = anno[object_id][cam_id]["annotation"][frame_id]["width"]
+                height = anno[object_id][cam_id]["annotation"][frame_id]["height"]
+                encoded_mask = anno[object_id][cam_id]["annotation"][frame_id]["encodedMask"]
+                coco_mask = decode_mask(width, height, encoded_mask)
+                new_anno[object_id][cam_id][frame_id] = coco_mask
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--takepath",
+        help="EgoExo take data root",
+        required=True
+    )
+    parser.add_argument(
+        "--annotationpath",
+        help="Annotations json file path",
+        required=True
+    )
+    parser.add_argument(
+        "--split_path",
+        help="path to split.json",
+        required=True
+    )
+    parser.add_argument(
+        "--split",
+        help="train/val/test split to process",
+        required=True
+    )
+    parser.add_argument(
+        "--outputpath",
+        help="Output data root",
+        required=True
+        )
+    args = parser.parse_args()
+
+    with open(args.split_path, "r") as fp:
+        data_split = json.load(fp)
+    # take_list = data_split[args.split]
+    take_list = ['09417ca4-3572-4ba1-a1db-7eaf3bd0b1c8'] # debug
+
+    os.makedirs(args.outputpath, exist_ok=True)
+    # Read the annotation file
+    with open(args.annotationpath, "r") as f:
+        annos = json.load(f)
+    annos = annos['annotations']
+
+    start = time()
+
+    for take_id in take_list:
+        if os.path.exists(f"{args.outputpath}/{take_id}"):
+            print(f"{take_id} already done!")
+            continue
+
+        # Create the output folder
+        os.makedirs(f"{args.outputpath}/{take_id}", exist_ok=True)
+        new_anno = {}
+        # Get the corresponding take name
+        anno = annos[take_id]
+        take_name = anno["take_name"]
+
+        valid_cams = set()
+        for x in anno['object_masks'].keys(): 
+            valid_cams.update(set(anno['object_masks'][x].keys()))
+        
+        ego_cams = []
+        exo_cams = []
+        for vc in valid_cams:
+            if 'aria' in vc:
+                ego_cams.append(vc)
+            else:
+                exo_cams.append(vc)
+
+        if len(ego_cams) > 1:
+            print(take_id, 'HAS MORE THAN ONE EGO')
+            breakpoint()
+        print(f"Processing take {take_id} {take_name}")
+        
+        # Process the masks
+        print("Start processing masks")
+        # new_anno["masks"] = {} # debug
+        # processMask(anno['object_masks'], new_anno["masks"])
+
+        # # Process the videos
+        print("Start processing Videos")
+        subsample_idx = processVideo(args.takepath, take_name, ego_cam=ego_cams[0], exo_cams=exo_cams, outputpath=args.outputpath, take_id=take_id)
+        if subsample_idx == -1:
+            print(f"{args.takepath}/{take_name}/frame_aligned_videos/{ego_cams[0]}.mp4 does not exist")
+            continue
+        # new_anno["subsample_idx"] = subsample_idx # debug
+
+        # Save the annotation
+        # with open(f"{args.outputpath}/{take_id}/annotation.json", "w") as f: # debug
+        #     json.dump(new_anno, f)
+    
+    end = time()
+    print(f"Total time: {end-start} seconds")

correspondence/SegSwap/data/process_data2.py

@@ -0,0 +1,175 @@
+import os
+import json
+from lzstring import LZString
+from pycocotools import mask as mask_utils
+import numpy as np
+from PIL import Image
+from decord import VideoReader
+from decord import cpu
+import argparse
+import cv2
+from time import time
+
+def save_frames(frames, frame_idxes, output_folder, is_aria=False):
+    # resize and save frames
+    scale = 4
+    if is_aria:
+        scale = 2
+
+    for img, fidx in zip(frames, frame_idxes):
+        H, W, C = img.shape
+        if H < 1408:
+            break
+        img2 = cv2.resize(img, (W//scale, H//scale))
+        cv2.imwrite(os.path.join(output_folder, f'{fidx}.jpg'), img2)
+
+def processVideo(takepath, take_name, ego_cam, exo_cams, outputpath, take_id):
+
+    # if not os.path.exists(f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4"):
+    #     return -1 
+    print("take_name:", take_name) # debug
+
+    # Subsample the ego video
+    # vr = VideoReader(
+    #     f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4", ctx=cpu(0)
+    # )
+    # len_video = len(vr)
+    # subsampling at 1fps -- none of the videos are annotated at more than 1 fps
+    # subsample_idx = np.arange(0, len_video, 1) # debug
+    subsample_idx = np.arange(3510, 4111, 1) # debug
+    
+    # if not os.path.exists(f"{outputpath}/{take_id}/{ego_cam}"):
+    #     os.makedirs(f"{outputpath}/{take_id}/{ego_cam}")
+    #     frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+    #     save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{ego_cam}", is_aria=True)
+
+    # Subsample the exo videos
+    for exo_cam in ["cam03"]:
+        vr = VideoReader(
+            f"{takepath}/{take_name}/frame_aligned_videos/{exo_cam}.mp4", ctx=cpu(0)
+        )
+        
+        os.makedirs(f"{outputpath}/{take_id}/{exo_cam}", exist_ok=True)
+        frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+
+        save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{exo_cam}", is_aria=False)
+
+    return subsample_idx.tolist()
+
+# def decode_mask(width, height, encoded_mask):
+#     try: 
+#         decomp_string = LZString.decompressFromEncodedURIComponent(encoded_mask)
+#     except:
+#         return None
+#     decomp_encoded = decomp_string.encode()
+#     rle_obj = {
+#         "size": [height, width],
+#         "counts": decomp_encoded,
+#     }       
+#     rle_obj['counts'] = rle_obj['counts'].decode('ascii')
+#     return rle_obj
+
+# def processMask(anno, new_anno):
+#     for object_id in anno.keys():
+#         new_anno[object_id] = {}
+#         for cam_id in anno[object_id].keys():
+#             new_anno[object_id][cam_id] = {}
+#             for frame_id in anno[object_id][cam_id]["annotation"].keys():
+#                 width = anno[object_id][cam_id]["annotation"][frame_id]["width"]
+#                 height = anno[object_id][cam_id]["annotation"][frame_id]["height"]
+#                 encoded_mask = anno[object_id][cam_id]["annotation"][frame_id]["encodedMask"]
+#                 coco_mask = decode_mask(width, height, encoded_mask)
+#                 new_anno[object_id][cam_id][frame_id] = coco_mask
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--takepath",
+        help="EgoExo take data root",
+        required=True
+    )
+    parser.add_argument(
+        "--annotationpath",
+        help="Annotations json file path",
+        required=True
+    )
+    parser.add_argument(
+        "--split_path",
+        help="path to split.json",
+        required=True
+    )
+    parser.add_argument(
+        "--split",
+        help="train/val/test split to process",
+        required=True
+    )
+    parser.add_argument(
+        "--outputpath",
+        help="Output data root",
+        required=True
+        )
+    args = parser.parse_args()
+
+    with open(args.split_path, "r") as fp:
+        data_split = json.load(fp)
+    # take_list = data_split[args.split]
+    take_list = ['3c744ca5-c64a-4de3-8235-c2f542ac5056'] # debug
+
+    os.makedirs(args.outputpath, exist_ok=True)
+    # Read the annotation file
+    # with open(args.annotationpath, "r") as f:
+    #     annos = json.load(f)
+    # annos = annos['annotations']
+
+    start = time()
+
+    for take_id in take_list:
+        # if os.path.exists(f"{args.outputpath}/{take_id}"):
+        #     print(f"{take_id} already done!")
+        #     continue
+
+        # Create the output folder
+        os.makedirs(f"{args.outputpath}/{take_id}", exist_ok=True)
+        new_anno = {}
+        # Get the corresponding take name
+        # anno = annos[take_id]
+        # take_name = anno["take_name"]
+        take_name = "sfu_cooking022_2"
+
+        # valid_cams = set()
+        # for x in anno['object_masks'].keys(): 
+        #     valid_cams.update(set(anno['object_masks'][x].keys()))
+        
+        # ego_cams = []
+        # exo_cams = []
+        # for vc in valid_cams:
+        #     if 'aria' in vc:
+        #         ego_cams.append(vc)
+        #     else:
+        #         exo_cams.append(vc)
+        ego_cams = ["aria"]
+        exo_cams = ["cam03"] # debug
+        # if len(ego_cams) > 1:
+        #     print(take_id, 'HAS MORE THAN ONE EGO')
+        #     breakpoint()
+        print(f"Processing take {take_id} {take_name}")
+        
+        # Process the masks
+        print("Start processing masks")
+        # new_anno["masks"] = {} # debug
+        # processMask(anno['object_masks'], new_anno["masks"])
+
+        # # Process the videos
+        print("Start processing Videos")
+        subsample_idx = processVideo(args.takepath, take_name, ego_cam=ego_cams[0], exo_cams=exo_cams, outputpath=args.outputpath, take_id=take_id)
+        if subsample_idx == -1:
+            print(f"{args.takepath}/{take_name}/frame_aligned_videos/{ego_cams[0]}.mp4 does not exist")
+            continue
+        # new_anno["subsample_idx"] = subsample_idx # debug
+
+        # Save the annotation
+        # with open(f"{args.outputpath}/{take_id}/annotation.json", "w") as f: # debug
+        #     json.dump(new_anno, f)
+    
+    end = time()
+    print(f"Total time: {end-start} seconds")

correspondence/SegSwap/data/process_data_batch.py

@@ -0,0 +1,271 @@
+import os
+import json
+from lzstring import LZString
+from pycocotools import mask as mask_utils
+import numpy as np
+from PIL import Image
+from decord import VideoReader
+from decord import cpu
+import argparse
+import cv2
+from time import time
+
+def save_frames(frames, frame_idxes, output_folder, is_aria=False):
+    # resize and save frames
+    scale = 4
+    if is_aria:
+        scale = 2
+
+    for img, fidx in zip(frames, frame_idxes):
+        H, W, C = img.shape
+        if H < 1408:
+            break
+        img2 = cv2.resize(img, (W//scale, H//scale))
+        cv2.imwrite(os.path.join(output_folder, f'{fidx}.jpg'), img2)
+
+def processVideo(takepath, take_name, ego_cam, exo_cams, outputpath, take_id, fps=1, batch_size=1000):
+    """
+    处理视频并抽帧
+    
+    Args:
+        takepath: 输入视频路径
+        take_name: take名称
+        ego_cam: ego相机名称
+        exo_cams: exo相机列表
+        outputpath: 输出路径
+        take_id: take ID
+        fps: 采样帧率，1表示1fps，30表示30fps（每帧都采样）
+        batch_size: 批处理大小，避免内存问题
+    """
+    if not os.path.exists(f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4"):
+        return -1
+
+    # Subsample the ego video
+    vr = VideoReader(
+        f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4", ctx=cpu(0)
+    )
+    len_video = len(vr)
+    
+    # 根据fps设置采样间隔
+    if fps == 30:
+        # 30fps：每帧都采样
+        sample_interval = 1
+    elif fps == 1:
+        # 1fps：假设原视频是30fps，每30帧采样一次
+        sample_interval = 30
+    else:
+        # 其他fps：计算采样间隔
+        sample_interval = max(1, 30 // fps)
+    
+    subsample_idx = np.arange(0, len_video, sample_interval)
+    print(f"Video length: {len_video}, Sample interval: {sample_interval}, Total frames to extract: {len(subsample_idx)}")
+    
+    # 处理ego视频
+    if not os.path.exists(f"{outputpath}/{take_id}/{ego_cam}"):
+        os.makedirs(f"{outputpath}/{take_id}/{ego_cam}")
+        
+        # 分批处理以避免内存问题
+        for i in range(0, len(subsample_idx), batch_size):
+            batch_idx = subsample_idx[i:i+batch_size]
+            print(f"Processing ego cam batch {i//batch_size + 1}/{(len(subsample_idx) + batch_size - 1)//batch_size}")
+            
+            try:
+                frames = vr.get_batch(batch_idx).asnumpy()[...,::-1]
+                save_frames(frames=frames, frame_idxes=batch_idx, 
+                          output_folder=f"{outputpath}/{take_id}/{ego_cam}", is_aria=True)
+            except Exception as e:
+                print(f"Error processing ego cam batch: {e}")
+                continue
+
+    # Subsample the exo videos
+    for exo_cam in exo_cams:
+        if not os.path.isdir(f"{outputpath}/{take_id}/{exo_cam}"):
+            try:
+                vr_exo = VideoReader(
+                    f"{takepath}/{take_name}/frame_aligned_videos/{exo_cam}.mp4", ctx=cpu(0)
+                )
+            except Exception as e:
+                print(f"{exo_cam} not available: {e}")
+                continue
+                
+            os.makedirs(f"{outputpath}/{take_id}/{exo_cam}")
+            
+            # 分批处理exo视频
+            for i in range(0, len(subsample_idx), batch_size):
+                batch_idx = subsample_idx[i:i+batch_size]
+                print(f"Processing {exo_cam} batch {i//batch_size + 1}/{(len(subsample_idx) + batch_size - 1)//batch_size}")
+                
+                try:
+                    frames = vr_exo.get_batch(batch_idx).asnumpy()[...,::-1]
+                    save_frames(frames=frames, frame_idxes=batch_idx, 
+                              output_folder=f"{outputpath}/{take_id}/{exo_cam}", is_aria=False)
+                except Exception as e:
+                    print(f"Error processing {exo_cam} batch: {e}")
+                    continue
+
+    return subsample_idx.tolist()
+
+def decode_mask(width, height, encoded_mask):
+    try: 
+        decomp_string = LZString.decompressFromEncodedURIComponent(encoded_mask)
+    except:
+        return None
+    decomp_encoded = decomp_string.encode()
+    rle_obj = {
+        "size": [height, width],
+        "counts": decomp_encoded,
+    }       
+    rle_obj['counts'] = rle_obj['counts'].decode('ascii')
+    return rle_obj
+
+def processMask(anno, new_anno):
+    for object_id in anno.keys():
+        new_anno[object_id] = {}
+        for cam_id in anno[object_id].keys():
+            new_anno[object_id][cam_id] = {}
+            for frame_id in anno[object_id][cam_id]["annotation"].keys():
+                width = anno[object_id][cam_id]["annotation"][frame_id]["width"]
+                height = anno[object_id][cam_id]["annotation"][frame_id]["height"]
+                encoded_mask = anno[object_id][cam_id]["annotation"][frame_id]["encodedMask"]
+                coco_mask = decode_mask(width, height, encoded_mask)
+                new_anno[object_id][cam_id][frame_id] = coco_mask
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--takepath",
+        help="EgoExo take data root",
+        required=True
+    )
+    parser.add_argument(
+        "--annotationpath",
+        help="Annotations json file path",
+        required=True
+    )
+    parser.add_argument(
+        "--split_path",
+        help="path to split.json",
+        required=True
+    )
+    parser.add_argument(
+        "--split",
+        help="train/val/test split to process",
+        required=True
+    )
+    parser.add_argument(
+        "--outputpath",
+        help="Output data root",
+        required=True
+    )
+    parser.add_argument(
+        "--fps",
+        type=int,
+        default=1,
+        help="Sampling frame rate (1 for 1fps, 30 for 30fps)"
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=1000,
+        help="Batch size for processing frames to avoid memory issues"
+    )
+    args = parser.parse_args()
+
+    with open(args.split_path, "r") as fp:
+        data_split = json.load(fp)
+    
+    # 使用指定的split或调试用的单个take
+    if args.split in data_split:
+        take_list = data_split[args.split]
+    else:
+        # 调试模式
+        take_list = ['6ca51642-c089-4989-b5a3-07977ec927d7']
+        print(f"Debug mode: processing only {take_list}")
+
+    os.makedirs(args.outputpath, exist_ok=True)
+    
+    # Read the annotation file
+    with open(args.annotationpath, "r") as f:
+        annos = json.load(f)
+    annos = annos['annotations']
+
+    print(f"Processing {len(take_list)} takes at {args.fps}fps with batch size {args.batch_size}")
+    start = time()
+
+    for idx, take_id in enumerate(take_list):
+        print(f"\n=== Processing take {idx+1}/{len(take_list)}: {take_id} ===")
+        
+        if os.path.exists(f"{args.outputpath}/{take_id}"):
+            print(f"{take_id} already done!")
+            continue
+
+        # Create the output folder
+        os.makedirs(f"{args.outputpath}/{take_id}", exist_ok=True)
+        new_anno = {}
+        
+        # Get the corresponding take name
+        if take_id not in annos:
+            print(f"Take {take_id} not found in annotations!")
+            continue
+            
+        anno = annos[take_id]
+        take_name = anno["take_name"]
+
+        valid_cams = set()
+        for x in anno['object_masks'].keys(): 
+            valid_cams.update(set(anno['object_masks'][x].keys()))
+        
+        ego_cams = []
+        exo_cams = []
+        for vc in valid_cams:
+            if 'aria' in vc:
+                ego_cams.append(vc)
+            else:
+                exo_cams.append(vc)
+
+        if len(ego_cams) == 0:
+            print(f"No ego camera found for take {take_id}")
+            continue
+            
+        if len(ego_cams) > 1:
+            print(f"{take_id} HAS MORE THAN ONE EGO: {ego_cams}")
+            # 使用第一个ego相机
+            
+        print(f"Processing take {take_id} {take_name}")
+        print(f"Ego cams: {ego_cams}")
+        print(f"Exo cams: {exo_cams}")
+        
+        # Process the masks
+        print("Start processing masks")
+        new_anno["masks"] = {}
+        processMask(anno['object_masks'], new_anno["masks"])
+
+        # Process the videos
+        print("Start processing Videos")
+        subsample_idx = processVideo(
+            args.takepath, 
+            take_name, 
+            ego_cam=ego_cams[0], 
+            exo_cams=exo_cams, 
+            outputpath=args.outputpath, 
+            take_id=take_id,
+            fps=args.fps,
+            batch_size=args.batch_size
+        )
+        
+        if subsample_idx == -1:
+            print(f"{args.takepath}/{take_name}/frame_aligned_videos/{ego_cams[0]}.mp4 does not exist")
+            continue
+            
+        new_anno["subsample_idx"] = subsample_idx
+        new_anno["fps"] = args.fps
+
+        # Save the annotation
+        with open(f"{args.outputpath}/{take_id}/annotation.json", "w") as f:
+            json.dump(new_anno, f)
+            
+        print(f"Completed take {take_id}, extracted {len(subsample_idx)} frames")
+    
+    end = time()
+    print(f"\nTotal processing time: {end-start:.2f} seconds")
+    print(f"Average time per take: {(end-start)/len(take_list):.2f} seconds")

correspondence/SegSwap/data/process_data_new.py

@@ -0,0 +1,176 @@
+import os
+import json
+from lzstring import LZString
+from pycocotools import mask as mask_utils
+import numpy as np
+from PIL import Image
+from decord import VideoReader
+from decord import cpu
+import argparse
+import cv2
+from time import time
+from tqdm import tqdm
+
+
+def save_frames(frames, frame_idxes, output_folder, is_aria=False):
+    # resize and save frames
+    scale = 4
+    if is_aria:
+        scale = 2
+
+    for img, fidx in zip(frames, frame_idxes):
+        H, W, C = img.shape
+        if H < 1408:
+            break
+        img2 = cv2.resize(img, (W//scale, H//scale))
+        cv2.imwrite(os.path.join(output_folder, f'{fidx}.jpg'), img2)
+
+def processVideo(takepath, take_name, ego_cam, exo_cams, outputpath, take_id):
+
+    if not os.path.exists(f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4"):
+        return -1
+
+    # Subsample the ego video
+    vr = VideoReader(
+        f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4", ctx=cpu(0)
+    )
+    len_video = len(vr)
+    # subsampling at 1fps -- none of the videos are annotated at more than 1 fps
+    subsample_idx = np.arange(0, len_video, 30)
+    
+    if not os.path.exists(f"{outputpath}/{take_id}/{ego_cam}"):
+        os.makedirs(f"{outputpath}/{take_id}/{ego_cam}")
+        frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+        save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{ego_cam}", is_aria=True)
+
+    # Subsample the exo videos
+    for exo_cam in exo_cams:
+        if not os.path.isfile(f"{outputpath}/{take_id}/{exo_cam}.mp4"):
+            try:
+                vr = VideoReader(
+                    f"{takepath}/{take_name}/frame_aligned_videos/{exo_cam}.mp4", ctx=cpu(0)
+                )
+            except:
+                print(f"{exo_cam} not available")
+                continue
+            os.makedirs(f"{outputpath}/{take_id}/{exo_cam}")
+            frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+
+            save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{exo_cam}", is_aria=False)
+
+    return subsample_idx.tolist()
+
+def decode_mask(width, height, encoded_mask):
+    try: 
+        decomp_string = LZString.decompressFromEncodedURIComponent(encoded_mask)
+    except:
+        return None
+    decomp_encoded = decomp_string.encode()
+    rle_obj = {
+        "size": [height, width],
+        "counts": decomp_encoded,
+    }       
+    rle_obj['counts'] = rle_obj['counts'].decode('ascii')
+    return rle_obj
+
+def processMask(anno, new_anno):
+    for object_id in anno.keys():
+        new_anno[object_id] = {}
+        for cam_id in anno[object_id].keys():
+            new_anno[object_id][cam_id] = {}
+            for frame_id in anno[object_id][cam_id]["annotation"].keys():
+                width = anno[object_id][cam_id]["annotation"][frame_id]["width"]
+                height = anno[object_id][cam_id]["annotation"][frame_id]["height"]
+                encoded_mask = anno[object_id][cam_id]["annotation"][frame_id]["encodedMask"]
+                coco_mask = decode_mask(width, height, encoded_mask)
+                new_anno[object_id][cam_id][frame_id] = coco_mask
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--takepath",
+        help="EgoExo take data root",
+        required=True
+    )
+    parser.add_argument(
+        "--annotationpath",
+        help="Annotations json file path",
+        required=True
+    )
+    parser.add_argument(
+        "--split_path",
+        help="path to split.json",
+        required=True
+    )
+    parser.add_argument(
+        "--split",
+        help="train/val/test split to process",
+        required=True
+    )
+    parser.add_argument(
+        "--outputpath",
+        help="Output data root",
+        required=True
+        )
+    args = parser.parse_args()
+
+    with open(args.split_path, "r") as fp:
+        data_split = json.load(fp)
+    take_list = data_split[args.split]
+
+    os.makedirs(args.outputpath, exist_ok=True)
+    # Read the annotation file
+    with open(args.annotationpath, "r") as f:
+        annos = json.load(f)
+    annos = annos['annotations']
+
+    start = time()
+
+    for take_id in tqdm(take_list):
+        if os.path.exists(f"{args.outputpath}/{take_id}"):
+            print(f"{take_id} already done!")
+            continue
+
+        # Create the output folder
+        os.makedirs(f"{args.outputpath}/{take_id}", exist_ok=True)
+        new_anno = {}
+        # Get the corresponding take name
+        anno = annos[take_id]
+        take_name = anno["take_name"]
+
+        valid_cams = set()
+        for x in anno['object_masks'].keys(): 
+            valid_cams.update(set(anno['object_masks'][x].keys()))
+        
+        ego_cams = []
+        exo_cams = []
+        for vc in valid_cams:
+            if 'aria' in vc:
+                ego_cams.append(vc)
+            else:
+                exo_cams.append(vc)
+
+        if len(ego_cams) > 1:
+            print(take_id, 'HAS MORE THAN ONE EGO')
+            breakpoint()
+        print(f"Processing take {take_id} {take_name}")
+        
+        # Process the masks
+        print("Start processing masks")
+        new_anno["masks"] = {}
+        processMask(anno['object_masks'], new_anno["masks"])
+
+        # # Process the videos
+        print("Start processing Videos")
+        subsample_idx = processVideo(args.takepath, take_name, ego_cam=ego_cams[0], exo_cams=exo_cams, outputpath=args.outputpath, take_id=take_id)
+        if subsample_idx == -1:
+            print(f"{args.takepath}/{take_name}/frame_aligned_videos/{ego_cams[0]}.mp4 does not exist")
+            continue
+        new_anno["subsample_idx"] = subsample_idx
+
+        # Save the annotation
+        with open(f"{args.outputpath}/{take_id}/annotation.json", "w") as f:
+            json.dump(new_anno, f)
+    
+    end = time()
+    print(f"Total time: {end-start} seconds")

correspondence/SegSwap/data/process_data_new_chunk.py

@@ -0,0 +1,198 @@
+import os
+import json
+from lzstring import LZString
+from pycocotools import mask as mask_utils
+import numpy as np
+from PIL import Image
+from decord import VideoReader
+from decord import cpu
+import argparse
+import cv2
+from time import time
+from tqdm import tqdm
+
+
+chunk_size = 1000 # 超参数：一次读取的frames数目
+
+def save_frames(frames, frame_idxes, output_folder, is_aria=False):
+    # resize and save frames
+    scale = 4
+    if is_aria:
+        scale = 2
+
+    for img, fidx in zip(frames, frame_idxes):
+        H, W, C = img.shape
+        if H < 1408:
+            break
+        img2 = cv2.resize(img, (W//scale, H//scale))
+        cv2.imwrite(os.path.join(output_folder, f'{fidx}.jpg'), img2)
+
+def processVideo(takepath, take_name, ego_cam, exo_cams, outputpath, take_id):
+
+    if not os.path.exists(f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4"):
+        return -1
+
+    # Subsample the ego video
+    vr = VideoReader(
+        f"{takepath}/{take_name}/frame_aligned_videos/{ego_cam}.mp4", ctx=cpu(0)
+    )
+    len_video = len(vr)
+    # subsampling at 1fps -- none of the videos are annotated at more than 1 fps
+    subsample_idx = np.arange(0, len_video, 30)
+
+    
+    if not os.path.exists(f"{outputpath}/{take_id}/{ego_cam}"):
+        os.makedirs(f"{outputpath}/{take_id}/{ego_cam}")
+        if len(subsample_idx) > chunk_size:
+            frames = []
+            for i in range(0, len(subsample_idx), chunk_size):
+                batch_idx = subsample_idx[i:i+chunk_size]
+                batch_frames = vr.get_batch(batch_idx).asnumpy()[..., ::-1]
+                frames.extend(batch_frames)
+            frames = np.array(frames)
+            save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{ego_cam}", is_aria=True)
+        else:
+            frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+            save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{ego_cam}", is_aria=True)
+
+
+
+    # Subsample the exo videos
+    for exo_cam in exo_cams:
+        if not os.path.isfile(f"{outputpath}/{take_id}/{exo_cam}.mp4"):
+            try:
+                vr = VideoReader(
+                    f"{takepath}/{take_name}/frame_aligned_videos/{exo_cam}.mp4", ctx=cpu(0)
+                )
+            except:
+                print(f"{exo_cam} not available")
+                continue
+            os.makedirs(f"{outputpath}/{take_id}/{exo_cam}")
+            if len(subsample_idx) > chunk_size:
+                frames = []
+                for i in range(0, len(subsample_idx), chunk_size):
+                    batch_idx = subsample_idx[i:i+chunk_size]
+                    batch_frames = vr.get_batch(batch_idx).asnumpy()[..., ::-1]
+                    frames.extend(batch_frames)
+                frames = np.array(frames)
+                save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{exo_cam}", is_aria=False)
+            else:
+                frames = vr.get_batch(subsample_idx).asnumpy()[...,::-1]
+                save_frames(frames=frames, frame_idxes=subsample_idx, output_folder=f"{outputpath}/{take_id}/{exo_cam}", is_aria=False)
+
+    return subsample_idx.tolist()
+
+def decode_mask(width, height, encoded_mask):
+    try: 
+        decomp_string = LZString.decompressFromEncodedURIComponent(encoded_mask)
+    except:
+        return None
+    decomp_encoded = decomp_string.encode()
+    rle_obj = {
+        "size": [height, width],
+        "counts": decomp_encoded,
+    }       
+    rle_obj['counts'] = rle_obj['counts'].decode('ascii')
+    return rle_obj
+
+def processMask(anno, new_anno):
+    for object_id in anno.keys():
+        new_anno[object_id] = {}
+        for cam_id in anno[object_id].keys():
+            new_anno[object_id][cam_id] = {}
+            for frame_id in anno[object_id][cam_id]["annotation"].keys():
+                width = anno[object_id][cam_id]["annotation"][frame_id]["width"]
+                height = anno[object_id][cam_id]["annotation"][frame_id]["height"]
+                encoded_mask = anno[object_id][cam_id]["annotation"][frame_id]["encodedMask"]
+                coco_mask = decode_mask(width, height, encoded_mask)
+                new_anno[object_id][cam_id][frame_id] = coco_mask
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--takepath",
+        help="EgoExo take data root",
+        required=True
+    )
+    parser.add_argument(
+        "--annotationpath",
+        help="Annotations json file path",
+        required=True
+    )
+    parser.add_argument(
+        "--split_path",
+        help="path to split.json",
+        required=True
+    )
+    parser.add_argument(
+        "--split",
+        help="train/val/test split to process",
+        required=True
+    )
+    parser.add_argument(
+        "--outputpath",
+        help="Output data root",
+        required=True
+        )
+    args = parser.parse_args()
+
+    with open(args.split_path, "r") as fp:
+        data_split = json.load(fp)
+    take_list = data_split[args.split]
+
+    os.makedirs(args.outputpath, exist_ok=True)
+    # Read the annotation file
+    with open(args.annotationpath, "r") as f:
+        annos = json.load(f)
+    annos = annos['annotations']
+
+    start = time()
+
+    for take_id in tqdm(take_list):
+        if os.path.exists(f"{args.outputpath}/{take_id}"):
+            print(f"{take_id} already done!")
+            continue
+
+        # Create the output folder
+        os.makedirs(f"{args.outputpath}/{take_id}", exist_ok=True)
+        new_anno = {}
+        # Get the corresponding take name
+        anno = annos[take_id]
+        take_name = anno["take_name"]
+
+        valid_cams = set()
+        for x in anno['object_masks'].keys(): 
+            valid_cams.update(set(anno['object_masks'][x].keys()))
+        
+        ego_cams = []
+        exo_cams = []
+        for vc in valid_cams:
+            if 'aria' in vc:
+                ego_cams.append(vc)
+            else:
+                exo_cams.append(vc)
+
+        if len(ego_cams) > 1:
+            print(take_id, 'HAS MORE THAN ONE EGO')
+            breakpoint()
+        print(f"Processing take {take_id} {take_name}")
+        
+        # Process the masks
+        print("Start processing masks")
+        new_anno["masks"] = {}
+        processMask(anno['object_masks'], new_anno["masks"])
+
+        # # Process the videos
+        print("Start processing Videos")
+        subsample_idx = processVideo(args.takepath, take_name, ego_cam=ego_cams[0], exo_cams=exo_cams, outputpath=args.outputpath, take_id=take_id)
+        if subsample_idx == -1:
+            print(f"{args.takepath}/{take_name}/frame_aligned_videos/{ego_cams[0]}.mp4 does not exist")
+            continue
+        new_anno["subsample_idx"] = subsample_idx
+
+        # Save the annotation
+        with open(f"{args.outputpath}/{take_id}/annotation.json", "w") as f:
+            json.dump(new_anno, f)
+    
+    end = time()
+    print(f"Total time: {end-start} seconds")

correspondence/SegSwap/data/split.json

@@ -0,0 +1 @@
+{"train": ["7d6fa5c7-a206-4b36-889b-44bed72b0cc8", "f9dbdf83-0135-4fdd-973a-f207c79b3453", "2941605e-7cfb-441f-92b7-b4ee187edeb6", "1f56f60f-9b36-493f-902f-a3bba0fb1b84", "f1c3d2f5-e6bf-4899-a40d-12489d1ebb11", "b054a313-c2e5-495f-add2-d3c265a030e8", "9a6dd2f3-8c46-44da-b424-cb3442be0f00", "9759968e-f72c-43b4-950e-3f715fdcd4ee", "095f8e44-e72c-41b1-b8b7-e967bb389f82", "dd0539a4-6c12-4ae9-987b-d41351d874a9", "66fbbbae-c740-40d1-ab71-1f882453f2c5", "819523fa-f601-4ed3-aa1f-b1df5f0482bf", "45dd51ae-9049-4cf0-9366-8d0094791f0b", "2d146c0f-88df-483d-8a26-efb052b9a8ac", "bafe400a-8433-4f69-8989-fabd82276b15", "8bb19b57-b9bf-4287-beaf-6a6d625712b6", "27eb4f84-28e8-46f7-9263-ff15fa9b894f", "1c9a978e-3921-44e5-8eab-f9e5401822cf", "f2f93854-2634-449c-b68e-aebf4743ac9f", "99820fc1-ce50-41ea-bc8d-37b563f9743b", "f85ff6cc-b530-49fe-89e4-ae8533a4d47e", "1f34bb6a-70ba-4e7c-ae19-b5086fc1176a", "15a055d2-a5e1-4ba9-9e2b-5a6282fc7f32", "57dee964-b77f-430b-8f37-fa4e5ba5c6a3", "0eac0520-a5d8-4a45-bf08-112f5f472850", "abcd9473-0885-43cc-9e4e-f632f36871d2", "d8967e18-31e6-49ec-b00b-00250320ee18", "27cf1b12-9114-46b5-b1fb-66fcef4294f6", "9c0fe0eb-064b-4393-bd60-55be9d0de7b0", "8a0ea233-beeb-42cf-84b8-d25bda4ad967", "975b64c9-60e8-426e-9d55-2323a2505b12", "eccc2a61-a64b-4a53-af79-e432406d9062", "fa984bda-fd07-4de9-9720-f4f0af8c5f96", "4af687ac-9602-48b9-acaf-c0085bc2fe53", "1e0d9cb9-849f-4ca7-beeb-b13a5d4c6668", "424d48e3-b2cc-4558-b9e8-f954f3a61f7a", "72fa7531-64a3-45fe-8e50-80906ffeb40a", "c79a0feb-6eb8-4eef-82c0-08f0d672cc79", "f69df63c-9cd3-442f-bc9b-5ba64b988da4", "4641c429-4150-415c-a824-bf42a22d1dbf", "7d59164c-e0bc-4ae0-95c9-733e4c8b0d6a", "cace80a1-42df-4cf4-a1ec-80647638a443", "1e15d070-a035-41a4-b37a-d82da76cd915", "2fbd4068-86e0-4893-b9c6-10c88fa8275c", "fa0e1a71-2174-43ef-a694-a4af360573a4", "a127cf90-51bf-46af-b0ee-ec932ed0b110", "c11735ca-6d22-4d16-972b-f55f8271f373", "3f6a08d0-b2fe-4625-9cb1-531622f76128", "6023b80a-a1af-405b-ae86-6613452a1512", "8ee798db-d02b-46ab-86f9-2091da1fb85e", "c922cd8a-192b-4b1c-b574-7a6bc1b89383", "0b05324a-47a2-456a-9f7a-1c36519d86e0", "d430ec80-4f9e-4ee8-82ba-b66f4a794289", "03bfe7f8-23b1-440a-bab5-45eed703d08d", "8fa671be-2624-4783-8572-5f4b7722b6c0", "2336980a-6599-4f18-8a4c-6f2aa92ed536", "cc0a8c8c-f9c6-4173-ad44-f04e7b8f2f46", "a91ef958-df79-4905-8469-37c552c11441", "98f58f0f-53d6-4e41-bf41-d8d74ccbc37c", "ca5b4327-9454-49db-b37b-7bf8b13784dd", "42214e49-7e6b-4164-9fd1-315e3ac8fe40", "de9efe94-a548-4e07-8a37-7a60abb40dac", "09ca3310-232c-4f3f-b201-3632ea8c6b75", "ca1434ea-b787-44ad-a9da-e0f7d5167a35", "3566c03b-5c4d-4fb8-b73c-0117a95cafb5", "127941e8-52d5-494f-901b-af3521a67ae3", "93a6cc04-f932-4555-93de-bf55a089ac80", "49127749-c922-40a0-8eb8-a689a11a68cf", "629a4ed5-9d3f-4a61-b699-99e77a03b449", "648ae12c-91f5-4a72-b6a3-d233b83ef654", "0872b2cd-933d-4a0d-8561-9b3d60361203", "a993f2d4-bed8-47aa-8ce2-7a08b9785b24", "8ee393cb-d4b3-47ae-a3d0-0e9f8bb912d1", "8634c58a-95ee-4aed-aa80-9392073815cf", "bbc13004-11f9-4a73-ab06-3fa3836d518f", "a2fc6bb5-ad56-460e-a8ae-64db8080d5ae", "1ec1409c-a3fd-46ab-8698-99f9317e9f13", "505d3491-c597-4622-96cf-123a30dfb2db", "cfd2c825-45d1-4e59-b33f-b6dff8c174c8", "a261cc1d-7a45-479f-81a9-7c73eb379e6c", "b63da48d-f6a2-4b94-a9af-3e694ca549f2", "de438b6d-8692-44ea-a6a3-0fbcc459a525", "126eb2a4-4d4f-4d35-b390-7d9550b54c60", "1c73aa9f-c23a-479f-9980-3cba6792f27c", "a4b63d2e-e149-4256-b951-eb9947420030", "d03179da-1fd6-40f1-9ed2-8c2681ca2a0c", "853d087c-f00d-4659-8d63-f5052fb465a6", "44e3a281-953f-439b-aa58-01a5a362a31f", "b4298585-d4af-48ef-b59b-8e878f66bff3", "39d48b6a-66e8-4bbb-a596-4461b601cabc", "6ba1dc31-7a08-4e92-aaad-5954bce6a7da", "8ad67099-9fd2-40aa-868c-3c458d238dbb", "b09efa75-5692-4490-bdf1-b3b2c0fed141", "c9230a3e-6f26-4f2f-bbfa-9daedb658568", "9a7c75f7-c02a-441e-ad6d-e9d413bb5861", "29f2335b-f53d-4329-ad26-25f2ae7d4e6e", "794b3bd3-eac9-4d0d-9789-bd068bff3944", "87531064-1b80-40c5-896c-344cb3200d5a", "38246505-8781-46e8-bbe6-838a3d7df20c", "45199fd8-dfda-446d-b1c3-f5e4deadc441", "16b05645-80cd-45e6-ad95-c9a9cc035d6d", "f4dead01-fa3d-4aa5-8b59-13a0d9186dd2", "86771809-b5aa-49dd-8c94-16d145f4819e", "717a56dc-5151-4ad9-88fd-8ac4aa3589a9", "a8d04142-fc0b-4ad4-acaa-8c17424411ff", "519a22f6-92df-4b85-b0cf-3c19bb311a1a", "b9519252-bc68-4d3f-9781-81426234e671", "74a85d9d-234c-40f8-b15d-03f222a4db19", "ab52c59f-e6ee-4f3f-92ac-5a6db630f8fd", "478b347f-98de-4953-bf9f-56b2ec386f81", "114984e0-4de8-4db7-afc8-0c66926f65f4", "4cf43506-d0a6-4c42-9136-adb2ecd57411", "d47c1c99-2df4-4ea4-8259-4952960d5129", "aef3aa96-9883-4119-9ae8-c8b921a98918", "2e10a473-32d3-4b73-99f9-d35717ccab91", "89815623-8ece-4e3c-8879-f1f32b299527", "b199d52d-4fc7-4ac3-b9c4-b5c84014dc1b", "e6bbb6dc-94c1-4e80-9e46-2b25f40e0c1c", "2a160c63-2acd-463a-8431-3dbf77d6de8e", "410ccf87-4217-4d87-836b-4af20b355d87", "b9bad7dc-c6f5-4488-b9d0-b787625b674f", "5df0a1b2-0240-4ddb-a6a8-2c48c80d7427", "4c08e855-f8ad-4c27-8ae7-116eb13f1309", "23ad5c7b-7413-4614-846a-976c120d8bcd", 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correspondence/SegSwap/data/video2img.py

@@ -0,0 +1,54 @@
+import cv2
+import os
+
+def save_video_frames(video_path, output_dir, prefix="frame", start_frame=0, step=1):
+    """
+    将MP4视频逐帧提取并保存为图片。
+    
+    Args:
+        video_path (str): 输入视频的路径（MP4格式）。
+        output_dir (str): 输出图片文件夹路径。
+        prefix (str): 保存图片的文件名前缀。
+        start_frame (int): 从第几帧开始提取。
+        step (int): 提取帧的间隔（默认为1，即每帧保存）。
+    """
+    # 检查视频文件是否存在
+    if not os.path.isfile(video_path):
+        print(f"视频文件 {video_path} 不存在！")
+        return
+
+    # 创建输出目录
+    os.makedirs(output_dir, exist_ok=True)
+
+    # 打开视频文件
+    video_capture = cv2.VideoCapture(video_path)
+
+    # 获取视频的总帧数
+    total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    print(f"视频总帧数: {total_frames}")
+
+    # 从指定的起始帧开始读取
+    frame_idx = 0
+    saved_frame_count = 0
+
+    while True:
+        success, frame = video_capture.read()
+        if not success:  # 读取失败或到达视频末尾
+            break
+
+        if frame_idx >= start_frame and (frame_idx - start_frame) % step == 0:
+            output_filename = os.path.join(output_dir, f"{prefix}_{frame_idx:06d}.jpg")
+            cv2.imwrite(output_filename, frame)
+            saved_frame_count += 1
+            print(f"保存帧 {frame_idx} 到 {output_filename}")
+
+        frame_idx += 1
+
+    video_capture.release()
+    print(f"完成！共保存 {saved_frame_count} 帧到 {output_dir}")
+
+
+# 使用示例
+video_path = "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/data/input2.mp4"  # 替换为您的MP4视频路径
+output_dir = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/multi_view_data_2"       # 替换为您希望保存图片的文件夹
+save_video_frames(video_path, output_dir, prefix="video_frame", start_frame=0, step=30)

correspondence/SegSwap/data/video_extract.sh

@@ -0,0 +1,16 @@
+#!/bin/bash
+
+# 定义路径变量，方便修改
+TAKE_PATH="/work/yuqian_fu/Ego/egoexo4d_keystep/takes"
+ANNOTATION_PATH="/work/yuqian_fu/Ego/egoexo4d_relations/annotations/relations_val.json"
+SPLIT_PATH="/work/yuqian_fu/Ego/data_segswap/split.json"
+SPLIT="val"
+OUTPUT_PATH="/work/yuqian_fu/Ego/camera_ready_iccv_0923"
+
+# 顺序执行三条命令，前一个成功才执行下一个
+python process_data.py --takepath $TAKE_PATH --annotationpath $ANNOTATION_PATH --split_path $SPLIT_PATH --split $SPLIT --outputpath $OUTPUT_PATH && \
+python process_data2.py --takepath $TAKE_PATH --annotationpath $ANNOTATION_PATH --split_path $SPLIT_PATH --split $SPLIT --outputpath $OUTPUT_PATH && \
+python only_extract_frames.py --takepath $TAKE_PATH --annotationpath $ANNOTATION_PATH --split_path $SPLIT_PATH --split $SPLIT --outputpath $OUTPUT_PATH
+
+echo "All commands executed successfully."
+

correspondence/SegSwap/environment.yml

@@ -0,0 +1,145 @@
+name: segswap
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1
+  - _openmp_mutex=5.1
+  - asttokens=2.2.1
+  - backcall=0.2.0
+  - backports=1.0
+  - backports.functools_lru_cache=1.6.5
+  - bzip2=1.0.8
+  - ca-certificates=2023.5.7
+  - decorator=5.1.1
+  - entrypoints=0.4
+  - executing=1.2.0
+  - ipykernel=6.15.0
+  - ipython=8.14.0
+  - jedi=0.18.2
+  - jupyter_client=7.3.4
+  - jupyter_core=5.3.1
+  - ld_impl_linux-64=2.38
+  - libffi=3.4.4
+  - libgcc-ng=11.2.0
+  - libgomp=11.2.0
+  - libsodium=1.0.18
+  - libstdcxx-ng=11.2.0
+  - libuuid=1.41.5
+  - matplotlib-inline=0.1.6
+  - ncurses=6.4
+  - nest-asyncio=1.5.6
+  - openssl=3.0.9
+  - packaging=23.1
+  - parso=0.8.3
+  - pexpect=4.8.0
+  - pickleshare=0.7.5
+  - platformdirs=3.8.0
+  - prompt-toolkit=3.0.38
+  - prompt_toolkit=3.0.38
+  - ptyprocess=0.7.0
+  - pure_eval=0.2.2
+  - pygments=2.15.1
+  - python=3.10.11
+  - python-dateutil=2.8.2
+  - python_abi=3.10
+  - readline=8.2
+  - six=1.16.0
+  - sqlite=3.41.2
+  - stack_data=0.6.2
+  - tk=8.6.12
+  - traitlets=5.9.0
+  - typing-extensions=4.6.3
+  - typing_extensions=4.6.3
+  - wcwidth=0.2.6
+  - xz=5.4.2
+  - zeromq=4.3.4
+  - zlib=1.2.13
+  - pip:
+    - absl-py==1.4.0
+    - appdirs==1.4.4
+    - cachetools==5.3.1
+    - certifi==2023.5.7
+    - charset-normalizer==3.1.0
+    - click==8.1.4
+    - cmake==3.26.4
+    - contourpy==1.1.0
+    - cycler==0.11.0
+    - debugpy==1.5.1
+    - decord==0.6.0
+    - docker-pycreds==0.4.0
+    - filelock==3.12.2
+    - fonttools==4.40.0
+    - gitdb==4.0.10
+    - gitpython==3.1.31
+    - google-auth==2.22.0
+    - google-auth-oauthlib==1.0.0
+    - grpcio==1.56.2
+    - idna==3.4
+    - imageio==2.31.5
+    - jinja2==3.1.2
+    - joblib==1.2.0
+    - jupyter-core==5.3.1
+    - kiwisolver==1.4.4
+    - kornia==0.6.12
+    - lazy-loader==0.3
+    - lit==16.0.6
+    - markdown==3.4.3
+    - markupsafe==2.1.3
+    - matplotlib==3.7.1
+    - mpmath==1.3.0
+    - natsort==8.4.0
+    - networkx==3.1
+    - numpy==1.25.0
+    - nvidia-cublas-cu11==11.10.3.66
+    - nvidia-cuda-cupti-cu11==11.7.101
+    - nvidia-cuda-nvrtc-cu11==11.7.99
+    - nvidia-cuda-runtime-cu11==11.7.99
+    - nvidia-cudnn-cu11==8.5.0.96
+    - nvidia-cufft-cu11==10.9.0.58
+    - nvidia-curand-cu11==10.2.10.91
+    - nvidia-cusolver-cu11==11.4.0.1
+    - nvidia-cusparse-cu11==11.7.4.91
+    - nvidia-nccl-cu11==2.14.3
+    - nvidia-nvtx-cu11==11.7.91
+    - oauthlib==3.2.2
+    - opencv-python==4.7.0.72
+    - pandas==2.0.3
+    - pathtools==0.1.2
+    - pillow==9.5.0
+    - pip==23.1.2
+    - protobuf==4.23.4
+    - psutil==5.9.0
+    - pyasn1==0.5.0
+    - pyasn1-modules==0.3.0
+    - pycocotools==2.0.6
+    - pyparsing==3.1.0
+    - pytz==2023.3
+    - pyyaml==6.0
+    - pyzmq==25.1.0
+    - requests==2.31.0
+    - requests-oauthlib==1.3.1
+    - rsa==4.9
+    - scikit-image==0.22.0
+    - scikit-learn==1.2.2
+    - scipy==1.11.0
+    - sentry-sdk==1.27.1
+    - setproctitle==1.3.2
+    - setuptools==67.8.0
+    - smmap==5.0.0
+    - sympy==1.12
+    - tensorboard==2.13.0
+    - tensorboard-data-server==0.7.1
+    - tensorboardx==2.6.1
+    - threadpoolctl==3.1.0
+    - tifffile==2023.9.26
+    - torch==2.0.1
+    - torchvision==0.15.2
+    - tornado==6.1
+    - tqdm==4.65.0
+    - triton==2.0.0
+    - tzdata==2023.3
+    - urllib3==1.26.16
+    - wandb==0.15.5
+    - werkzeug==2.3.6
+    - wheel==0.38.4

correspondence/SegSwap/eval_handal.py

@@ -0,0 +1,252 @@
+# coding=utf-8
+import os 
+import sys 
+sys.path.append('/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model')
+import numpy as np 
+from PIL import Image
+import cv2
+import json
+import random
+import torch 
+import torch.nn.functional as F
+import torchvision.models as models
+import torchvision.transforms as transforms
+
+import transformer # model
+
+import tqdm
+
+from pycocotools import mask as mask_utils
+from torch.cuda.amp import autocast
+
+
+MASKThresh = 0.5
+root_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL"
+json_path = "/data/work-gcp-europe-west4-a/yuqian_fu/datasets/HANDAL/handal_test_all.json"
+with open(json_path, "r") as fp:
+    datas = json.load(fp)
+
+
+def reshape_img_war(img, size=(480, 480)):
+    C = 1
+    if len(img.shape) == 2:
+        H, W = img.shape
+        img = img[..., None]
+    else:
+        H, W, C = img.shape
+    
+    temp = np.zeros((max(H, W), max(H, W), C), dtype=np.uint8)
+
+    if H > W:
+        L = (H - W) // 2
+        temp[:, L:-L] = img
+    elif W > H:
+        L = (W - H) // 2
+        temp[L:-L] = img
+    else:
+        temp = img
+
+    temp = cv2.resize(temp, size, interpolation=cv2.INTER_NEAREST)
+
+    return temp
+
+def get_model(model_path):
+    ## set gpu
+    device = torch.device('cuda')
+
+    backbone = models.resnet50(pretrained=False)   
+    resnet_feature_layers = ['conv1','bn1','relu','maxpool','layer1','layer2','layer3']
+    resnet_module_list = [getattr(backbone,l) for l in resnet_feature_layers]
+    last_layer_idx = resnet_feature_layers.index('layer3')
+    backbone = torch.nn.Sequential(*resnet_module_list[:last_layer_idx+1])
+
+    ## load pre-trained weight
+    pos_weight = 0.1
+    feat_weight = 1
+    dropout = 0.1
+    activation = 'relu'
+    mode = 'small'
+    layer_type = ['I', 'C', 'I', 'C', 'I', 'N']
+    drop_feat = 0.1
+    feat_dim=1024
+
+    ## model
+    netEncoder = transformer.TransEncoder(feat_dim,
+                                        pos_weight = pos_weight,
+                                        feat_weight = feat_weight,
+                                        dropout = dropout,
+                                        activation = activation,
+                                        mode = mode,
+                                        layer_type = layer_type,
+                                        drop_feat = drop_feat) 
+
+    netEncoder.to(device)
+
+    print ('Loading net weight from {}'.format(model_path))
+    param = torch.load(model_path)
+    backbone.load_state_dict(param['backbone'])
+    netEncoder.load_state_dict(param['encoder'])
+    backbone.eval()
+    netEncoder.eval()
+    backbone.to(device)
+    netEncoder.to(device)
+
+    return backbone, netEncoder 
+
+def get_tensors(I1np, I2np, M1np):
+
+    # masking Image1
+    I1np = I1np 
+
+    I1 = I1np
+    I2 = I2np
+
+    norm_mean, norm_std = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
+    transformINet = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(norm_mean, norm_std)])
+    tensor1 = I1 
+    tensor2 = I2 
+    tensor3 = M1np
+
+
+    tensor1 = transformINet(tensor1).unsqueeze(0).cuda()
+    tensor2 = transformINet(tensor2).unsqueeze(0).cuda()
+    tensor3 = torch.from_numpy(tensor3).unsqueeze(0).type(torch.FloatTensor).cuda()
+
+    return I1, I2, tensor1, tensor2, tensor3
+
+def forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3):
+    with torch.no_grad():
+        with autocast():
+            feat1 = backbone( tensor1 ) ## feature
+            feat1 = F.normalize(feat1, dim=1) ## l2 normalization
+            feat2 = backbone( tensor2 ) ## features
+            feat2 = F.normalize(feat2, dim=1) ## l2 normalization
+
+            fmask = backbone(tensor3.unsqueeze(0).repeat(1, 3, 1, 1))
+            fmask = F.normalize(fmask, dim=1)
+
+            out1, out2, out3 = netEncoder(feat1, feat2, fmask) ## predictions
+            m1_final, m2_final, m3_final = out1[0, 2].cpu().numpy(), out2[0, 2].cpu().numpy(), out3.item()
+    
+    return m1_final, m2_final, m3_final
+
+
+# ego2exo
+def egoexo(backbone, netEncoder, ego, exo, obj, pred_json):
+
+    pred_json['masks'][obj][f'{ego}_{exo}'] = {}
+    data_list = []
+    for data in datas:
+        if data["image"].split('/')[0] == obj:
+            data_list.append(data)
+    for idx, data in enumerate(data_list):
+        query_frame_tmp = cv2.imread(os.path.join(root_path, data["first_frame_image"]))[..., ::-1]
+        query_frame = Image.fromarray(reshape_img_war(query_frame_tmp))
+        ann_query = data["first_frame_anns"][0]
+        query_mask = mask_utils.decode(ann_query["segmentation"])
+        query_mask = reshape_img_war(query_mask)
+
+        target_frame_tmp = cv2.imread(os.path.join(root_path, data["image"]))[..., ::-1]
+        target_frame = Image.fromarray(reshape_img_war(target_frame_tmp))
+
+        Ix, Iy, tensor1, tensor2, tensor3 = get_tensors(query_frame, target_frame, query_mask)
+        mx, my, confidence = forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3)
+
+        y_step = (my > MASKThresh)
+
+        target_pred = mask_utils.encode(np.asfortranarray(y_step.astype(np.uint8)))
+        target_pred['counts'] = target_pred['counts'].decode('ascii')
+        idx = str(idx)
+        pred_json['masks'][obj][f'{ego}_{exo}'][idx] = {'pred_mask': target_pred, 'confidence': confidence}
+        
+
+def evaluate_ours(backbone, netEncoder):
+    result_list = []
+    for data in tqdm.tqdm(datas):
+        query_frame_tmp = cv2.imread(os.path.join(root_path, data["first_frame_image"]))[..., ::-1]
+        query_frame = Image.fromarray(reshape_img_war(query_frame_tmp))
+        ann_query = data["first_frame_anns"][0]
+        query_mask = mask_utils.decode(ann_query["segmentation"])
+        query_mask = reshape_img_war(query_mask)
+
+        target_frame_tmp = cv2.imread(os.path.join(root_path, data["image"]))[..., ::-1]
+        target_frame = Image.fromarray(reshape_img_war(target_frame_tmp))
+
+        Ix, Iy, tensor1, tensor2, tensor3 = get_tensors(query_frame, target_frame, query_mask)
+        mx, my, confidence = forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3)
+
+        y_step = (my > MASKThresh)
+
+        target_pred = mask_utils.encode(np.asfortranarray(y_step.astype(np.uint8)))
+        target_pred['counts'] = target_pred['counts'].decode('ascii')
+        sample = {
+                'image': data["image"],
+                'anns': data["anns"],
+                'new_img_id': data["new_img_id"],
+                'pred_mask': target_pred
+            }
+        result_list.append(sample)
+    return result_list
+
+
+
+
+def main():
+
+    resume_path = '/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/moco_v2_800ep_pretrain_torchvision.pth.tar'
+    param = torch.load(resume_path)['model']
+    new_param = {}
+    for key in param.keys():
+        if 'fc' in key:
+            continue
+        new_param[key] = param[key]
+
+    backbone = models.resnet50(pretrained=False)
+    backbone.load_state_dict(new_param, strict=False)
+    resnet_feature_layers = ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']
+    resnet_module_list = [getattr(backbone, l) for l in resnet_feature_layers]
+    last_layer_idx = resnet_feature_layers.index('layer3')
+    backbone = torch.nn.Sequential(*resnet_module_list[:last_layer_idx + 1])
+    feat_dim = 1024
+    backbone.cuda()
+
+    ## model
+    netEncoder = transformer.TransEncoder(feat_dim,
+                                        pos_weight=0.1,
+                                        feat_weight=1,
+                                        dropout=0.1,
+                                        activation="relu",
+                                        mode="small",
+                                        layer_type=['I', 'C', 'I', 'C', 'I', 'N'],
+                                        drop_feat=0.1)
+
+    netEncoder.cuda()
+        
+    result_list = evaluate_ours(backbone, netEncoder)
+
+    return result_list
+
+if __name__ == '__main__':
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--out_path', type=str, required=True)
+
+    args = parser.parse_args()
+
+    # objs = os.listdir(root_path)
+    # objs.remove("vis_rondom_check")
+    # objs = [file for file in objs if not file.endswith('.json')]
+    # objs = objs[:2]
+    # debug
+    # print(len(objs))
+
+    results = main()
+    print("data_num:", len(results))
+
+    os.makedirs(args.out_path, exist_ok=True)
+    with open(f"{args.out_path}/segswap_handal_pred_1.json", "w") as fp:
+        json.dump(results, fp)
+

correspondence/SegSwap/eval_segswap.py

@@ -0,0 +1,287 @@
+# coding=utf-8
+import os 
+import sys 
+sys.path.append('/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model')
+import numpy as np 
+from PIL import Image
+import cv2
+import json
+import random
+import torch 
+import torch.nn.functional as F
+import torchvision.models as models
+import torchvision.transforms as transforms
+
+import transformer # model
+
+import tqdm
+
+from pycocotools import mask as mask_utils
+from torch.cuda.amp import autocast
+
+
+MASKThresh = 0.5
+#out = ['f2f93854-2634-449c-b68e-aebf4743ac9f', '7d59164c-e0bc-4ae0-95c9-733e4c8b0d6a', 'cace80a1-42df-4cf4-a1ec-80647638a443', '8fa671be-2624-4783-8572-5f4b7722b6c0', 'ca1434ea-b787-44ad-a9da-e0f7d5167a35', 'cfd2c825-45d1-4e59-b33f-b6dff8c174c8', '39d48b6a-66e8-4bbb-a596-4461b601cabc', 'f4dead01-fa3d-4aa5-8b59-13a0d9186dd2', '4cf43506-d0a6-4c42-9136-adb2ecd57411', '89815623-8ece-4e3c-8879-f1f32b299527', '5fd383f1-c8bb-42d7-b98b-7418d99d9bb4', 'b1b794e8-7839-46ab-b05f-f4b1c16d5420', 'b7dbb47c-d850-4853-b434-7b20519ea9e5', '636eaa0b-d65d-4b25-bbdd-1065f84ef89e', 'd2b0ee95-2a76-4b69-aebf-3c7e553f8e2b', 'cea1b20b-6e18-4bb6-87e0-164a2b8c3dc0', 'fba2c124-99ec-40ed-8d6a-46808afe6d98', '549e5b97-f93a-4500-8f02-5be13017dce5', '6cbfa460-72a7-4038-b6af-4305a1cc05dd', '319a9983-f70c-4224-a3a7-33338c8a9f35', 'bcfb6839-3c09-4d42-9d9c-59042f6ab721', '3ca2798d-cfe3-46c6-a8bc-cc4689bd6d75', 'aa40670e-4487-4f60-a27b-26f7372ef8e7', '389cfa3f-3a4c-4b8f-9535-d7c95ffd594c', '214bdc0d-fa50-4a84-b771-c0a7bdeafadc', '08504348-4f72-477e-a08f-1050204ae55e', 'd35a162e-c38e-4017-94d1-539f26651115', '925ebe22-f97a-4e79-aed3-9873cf461c3c', 'c0b7d130-2004-4450-ad94-ea3167bd9fab', 'f1ce9be1-b623-4ce4-84e2-37e9f88eea86', 'c288cb15-81c7-4463-814f-959c12740499', 'b69de073-c157-4cff-8eba-97bfc7baa012', '289c4873-5bf9-41b9-b784-aba52b54cd4d', '0bcb8b46-cc45-4bcb-a627-85633e54e060', '819780c3-97c0-4ac6-b37f-8c66abf8167d', '9512a137-40b6-49c6-a03d-a3340b9dd277', '393e9b60-504c-4cc7-a90b-eb78dd62d5ff', 'fb09baec-1a5a-40b4-8d72-e581c93fbd77', '3042b472-99cc-4407-a79e-f76916b95737', '4939903a-2c73-4633-ae15-618be39990e7', 'ced0e340-b958-4505-badc-c8c2f256c145', 'e53ae33b-61b6-4c3e-8be0-5696f961704b', '759aa03c-c8e1-4fcb-8817-85948100ed33', '31df8578-1fd0-4406-9008-900a88f7990a', '9506c70a-1639-42ea-bbfc-2b9c0f8c9394', 'e46f9a53-7625-4827-8b92-79c958d3524e', '3125dca7-b99e-4b2b-8844-2d912619b353', 'b65a60e0-f224-4b3d-bf78-ba44e12c4ac1', 'c53daadd-09f0-485b-a51c-1d5679f5fb09', 'b378d186-0587-40ed-afdf-875e6dfb5876', '8000dfb2-accb-4bb8-abfb-cc2d677d0b2f', 'ff93ae48-0daa-418c-9d5c-b5b0b6d23efb', '6d0a7c80-ae8c-4673-8f70-c09fd6fbebe8', 'f0cd03a5-9cd8-4510-87c1-a5c493197b75', 'c16175f5-f990-48a1-9dcc-6a385f108687', '816dd81e-93b1-433c-83ff-264ae404a3bf', 'f8bed5fe-3e09-4885-9539-edb4d5b2279a', '601c9c61-fc2b-4ac2-b3c4-dda557c2563b', '56c35d79-acb1-47a1-8590-7e5cb2585ee5', 'd5193bae-a7f5-4e8a-9c96-09f557c7ea9d', '8d7646a3-ce7b-425d-b16e-9a63a1166576', '0b89efcd-59bf-4f0b-a81a-50dee0b79982', '9681b4c6-9713-4bb3-aa9a-7df7daa4e74d', '69fac17f-6527-493d-8dac-cd3bb61ce23e', '5ee00a17-171f-46a4-927b-3aa9d0fe176e']
+
+
+def reshape_img_war(img, size=(480, 480)):
+    C = 1
+    if len(img.shape) == 2:
+        H, W = img.shape
+        img = img[..., None]
+    else:
+        H, W, C = img.shape
+    
+    temp = np.zeros((max(H, W), max(H, W), C), dtype=np.uint8)
+
+    if H > W:
+        L = (H - W) // 2
+        temp[:, L:-L] = img
+    elif W > H:
+        L = (W - H) // 2
+        temp[L:-L] = img
+    else:
+        temp = img
+
+    temp = cv2.resize(temp, size, interpolation=cv2.INTER_NEAREST)
+
+    return temp
+
+def get_model(model_path):
+    ## set gpu
+    device = torch.device('cuda')
+
+    backbone = models.resnet50(pretrained=False)   
+    resnet_feature_layers = ['conv1','bn1','relu','maxpool','layer1','layer2','layer3']
+    resnet_module_list = [getattr(backbone,l) for l in resnet_feature_layers]
+    last_layer_idx = resnet_feature_layers.index('layer3')
+    backbone = torch.nn.Sequential(*resnet_module_list[:last_layer_idx+1])
+
+    ## load pre-trained weight
+    pos_weight = 0.1
+    feat_weight = 1
+    dropout = 0.1
+    activation = 'relu'
+    mode = 'small'
+    layer_type = ['I', 'C', 'I', 'C', 'I', 'N']
+    drop_feat = 0.1
+    feat_dim=1024
+
+    ## model
+    netEncoder = transformer.TransEncoder(feat_dim,
+                                        pos_weight = pos_weight,
+                                        feat_weight = feat_weight,
+                                        dropout = dropout,
+                                        activation = activation,
+                                        mode = mode,
+                                        layer_type = layer_type,
+                                        drop_feat = drop_feat) 
+
+    netEncoder.to(device)
+
+    print ('Loading net weight from {}'.format(model_path))
+    param = torch.load(model_path)
+    backbone.load_state_dict(param['backbone'])
+    netEncoder.load_state_dict(param['encoder'])
+    backbone.eval()
+    netEncoder.eval()
+    backbone.to(device)
+    netEncoder.to(device)
+
+    return backbone, netEncoder 
+
+def get_tensors(I1np, I2np, M1np):
+
+    # masking Image1
+    I1np = I1np 
+
+    I1 = I1np
+    I2 = I2np
+
+    norm_mean, norm_std = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
+    transformINet = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(norm_mean, norm_std)])
+    tensor1 = I1 
+    tensor2 = I2 
+    tensor3 = M1np
+
+
+    tensor1 = transformINet(tensor1).unsqueeze(0).cuda()
+    tensor2 = transformINet(tensor2).unsqueeze(0).cuda()
+    tensor3 = torch.from_numpy(tensor3).unsqueeze(0).type(torch.FloatTensor).cuda()
+
+    return I1, I2, tensor1, tensor2, tensor3
+
+def forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3):
+    with torch.no_grad():
+        with autocast():
+            feat1 = backbone( tensor1 ) ## feature
+            feat1 = F.normalize(feat1, dim=1) ## l2 normalization
+            feat2 = backbone( tensor2 ) ## features
+            feat2 = F.normalize(feat2, dim=1) ## l2 normalization
+
+            fmask = backbone(tensor3.unsqueeze(0).repeat(1, 3, 1, 1))
+            fmask = F.normalize(fmask, dim=1)
+
+            out1, out2, out3 = netEncoder(feat1, feat2, fmask) ## predictions
+            m1_final, m2_final, m3_final = out1[0, 2].cpu().numpy(), out2[0, 2].cpu().numpy(), out3.item()
+    
+    return m1_final, m2_final, m3_final
+
+
+def load_frame(path, frame_idx):
+    img = cv2.imread(os.path.join(path, f'{frame_idx}.jpg'))[..., ::-1]
+    img = Image.fromarray(reshape_img_war(img))
+    return img
+
+def egoexo(backbone, netEncoder, annotations, ego, exo, obj, take, anno_path, pred_json):
+
+    pred_json['masks'][obj][f'{ego}_{exo}'] = {}
+    for idx in annotations['masks'][obj][ego].keys():
+
+        ego_frame = load_frame(path=f'{anno_path}/{take}/{ego}/', frame_idx=idx)
+        ego_mask = mask_utils.decode(annotations['masks'][obj][ego][idx])
+        ego_mask = reshape_img_war(ego_mask)
+
+        exo_frame = load_frame(path=f'{anno_path}/{take}/{exo}/', frame_idx=idx)
+
+        Ix, Iy, tensor1, tensor2, tensor3 = get_tensors(ego_frame, exo_frame, ego_mask)
+        mx, my, confidence = forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3)
+
+        y_step = (my > MASKThresh)
+        
+
+        exo_pred = mask_utils.encode(np.asfortranarray(y_step.astype(np.uint8)))
+        exo_pred['counts'] = exo_pred['counts'].decode('ascii')
+        pred_json['masks'][obj][f'{ego}_{exo}'][idx] = {'pred_mask': exo_pred, 'confidence': confidence}
+
+def exoego(backbone, netEncoder, annotations, ego, exo, obj, take, anno_path, pred_json):
+
+    pred_json['masks'][obj][f'{exo}_{ego}'] = {}
+    for idx in annotations['masks'][obj][exo].keys():
+
+        exo_frame = load_frame(path=f'{anno_path}/{take}/{exo}/', frame_idx=idx)
+        exo_mask = mask_utils.decode(annotations['masks'][obj][exo][idx])
+        exo_mask = reshape_img_war(exo_mask)
+
+        ego_frame = load_frame(path=f'{anno_path}/{take}/{ego}/', frame_idx=idx)
+
+        Ix, Iy, tensor1, tensor2, tensor3 = get_tensors(exo_frame, ego_frame, exo_mask)
+        mx, my, confidence = forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3)
+
+        y_step = (my > MASKThresh)
+
+        ego_pred = mask_utils.encode(np.asfortranarray(y_step.astype(np.uint8)))
+        ego_pred['counts'] = ego_pred['counts'].decode('ascii')
+        pred_json['masks'][obj][f'{exo}_{ego}'][idx] = {'pred_mask': ego_pred, 'confidence': confidence}
+
+def main(model_path, takes, anno_path, out_path, setting='ego-exo', save_inter=False):
+
+    print('TOTAL TAKES: ', len(takes))
+
+    resume_path = '/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/moco_v2_800ep_pretrain_torchvision.pth.tar'
+    param = torch.load(resume_path)['model']
+    new_param = {}
+    for key in param.keys():
+        if 'fc' in key:
+            continue
+        new_param[key] = param[key]
+
+    backbone = models.resnet50(pretrained=False)
+    backbone.load_state_dict(new_param, strict=False)
+    resnet_feature_layers = ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']
+    resnet_module_list = [getattr(backbone, l) for l in resnet_feature_layers]
+    last_layer_idx = resnet_feature_layers.index('layer3')
+    backbone = torch.nn.Sequential(*resnet_module_list[:last_layer_idx + 1])
+    feat_dim = 1024
+    backbone.cuda()
+
+    ## model
+    netEncoder = transformer.TransEncoder(feat_dim,
+                                        pos_weight=0.1,
+                                        feat_weight=1,
+                                        dropout=0.1,
+                                        activation="relu",
+                                        mode="small",
+                                        layer_type=['I', 'C', 'I', 'C', 'I', 'N'],
+                                        drop_feat=0.1)
+
+    netEncoder.cuda()
+        
+    results = {}
+    for take in tqdm.tqdm(takes):
+        #修改
+        # if take in out:
+        #     continue
+
+        #resume机制
+        if os.path.exists(f'{out_path}/{take}'):
+            print("already done!")
+            continue
+
+        with open(f'{anno_path}/{take}/annotation.json', 'r') as fp:
+            annotations = json.load(fp)
+
+        pred_json = {'masks': {}, 'subsample_idx': annotations['subsample_idx']}
+
+        for obj in annotations['masks']:
+
+            pred_json['masks'][obj] = {}
+
+            cams = annotations['masks'][obj].keys()
+
+            exo_cams = [x for x in cams if 'aria' not in x]
+            ego_cams = [x for x in cams if 'aria' in x]
+
+            for ego in ego_cams:
+                for exo in exo_cams:
+                    # ego -> exo
+                    if setting == 'ego-exo':
+                        egoexo(backbone=backbone, netEncoder=netEncoder, annotations=annotations,
+                                ego=ego, exo=exo, obj=obj, take=take, anno_path=anno_path, pred_json=pred_json)
+                    elif setting == 'exo-ego':
+                        exoego(backbone=backbone, netEncoder=netEncoder, annotations=annotations,
+                                ego=ego, exo=exo, obj=obj, take=take, anno_path=anno_path, pred_json=pred_json)
+                    else:
+                        raise Exception(f"Setting {setting} not recognized.")
+
+        results[take] = pred_json
+
+        if save_inter:
+            os.makedirs(f'{out_path}/{take}', exist_ok=True)
+            with open(f'{out_path}/{take}/pred_annotations.json', 'w') as fp:
+                json.dump(pred_json, fp)
+
+    return results
+
+if __name__ == '__main__':
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--ckpt_path', type=str, required=True)
+    parser.add_argument('--data_path', type=str, required=True)
+    parser.add_argument('--splits_path', type=str, required=True, help="Path to json of take splits")
+    parser.add_argument('--split', type=str, required=True, help="Split to evaluate on")
+    parser.add_argument('--out_path', type=str, required=True)
+    parser.add_argument('--setting', required=True, choices=['ego-exo', 'exo-ego'], help="ego-exo or exo-ego")
+    parser.add_argument('--save_inter', action='store_true', help="Store intermediate take wise results")
+
+    args = parser.parse_args()
+
+    with open(args.splits_path, "r") as fp:
+        splits = json.load(fp)
+
+    takes = splits[args.split]
+    takes = random.sample(takes, 20)
+    results = main(args.ckpt_path,
+                takes,
+                args.data_path,
+                args.out_path,
+                setting=args.setting,
+                save_inter=args.save_inter)
+
+    os.makedirs(args.out_path, exist_ok=True)
+    with open(f"{args.out_path}/{args.setting}_{args.split}_results.json", "w") as fp:
+        json.dump({args.setting: {'results': results}}, fp)
+

correspondence/SegSwap/eval_segswap_new.py

@@ -0,0 +1,292 @@
+# coding=utf-8
+import os 
+import sys 
+sys.path.append('model/')
+import numpy as np 
+from PIL import Image
+import cv2
+import json
+
+import torch 
+import torch.nn.functional as F
+import torchvision.models as models
+import torchvision.transforms as transforms
+
+import transformer # model
+import random
+
+import tqdm
+
+from pycocotools import mask as mask_utils
+import utils_new
+
+# json_path = "/home/yuqian_fu/Projects/PSALM/filter_takes_byname_test.json"
+# with open(json_path, "r") as fp:
+#     datas = json.load(fp)
+# takes_filter = datas['soccer']
+splits_path = '/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/split.json'
+with open(splits_path, "r") as fp:
+    splits = json.load(fp)
+takes_filter = splits['test'] # ['ego-exo', 'exo-ego']
+np.random.seed(0)
+takes_filter = np.random.choice(takes_filter, 20, replace=False) # debug
+print("takes_filter:", takes_filter)
+MASKThresh = 0.5
+
+def reshape_img_war(img, size=(480, 480)):
+    C = 1
+    if len(img.shape) == 2:
+        H, W = img.shape
+        img = img[..., None]
+    else:
+        H, W, C = img.shape
+    
+    temp = np.zeros((max(H, W), max(H, W), C), dtype=np.uint8)
+
+    if H > W:
+        L = (H - W) // 2
+        temp[:, L:-L] = img
+    elif W > H:
+        L = (W - H) // 2
+        temp[L:-L] = img
+    else:
+        temp = img
+
+    temp = cv2.resize(temp, size, interpolation=cv2.INTER_NEAREST)
+
+    return temp
+
+def get_model(model_path):
+    ## set gpu
+    device = torch.device('cuda')
+
+    backbone = models.resnet50(pretrained=False)   
+    resnet_feature_layers = ['conv1','bn1','relu','maxpool','layer1','layer2','layer3']
+    resnet_module_list = [getattr(backbone,l) for l in resnet_feature_layers]
+    last_layer_idx = resnet_feature_layers.index('layer3')
+    backbone = torch.nn.Sequential(*resnet_module_list[:last_layer_idx+1])
+
+    ## load pre-trained weight
+    pos_weight = 0.1
+    feat_weight = 1
+    dropout = 0.1
+    activation = 'relu'
+    mode = 'small'
+    layer_type = ['I', 'C', 'I', 'C', 'I', 'N']
+    drop_feat = 0.1
+    feat_dim=1024
+
+    ## model
+    netEncoder = transformer.TransEncoder(feat_dim,
+                                        pos_weight = pos_weight,
+                                        feat_weight = feat_weight,
+                                        dropout = dropout,
+                                        activation = activation,
+                                        mode = mode,
+                                        layer_type = layer_type,
+                                        drop_feat = drop_feat) 
+
+    netEncoder.to(device)
+
+    print ('Loading net weight from {}'.format(model_path))
+    param = torch.load(model_path)
+    backbone.load_state_dict(param['backbone'])
+    netEncoder.load_state_dict(param['encoder'])
+    backbone.eval()
+    netEncoder.eval()
+    backbone.to(device)
+    netEncoder.to(device)
+
+    return backbone, netEncoder 
+
+def get_tensors(I1np, I2np, M1np):
+
+    # masking Image1
+    I1np = I1np 
+
+    I1 = I1np
+    I2 = I2np
+
+    norm_mean, norm_std = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
+    transformINet = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(norm_mean, norm_std)])
+    tensor1 = I1 
+    tensor2 = I2 
+    tensor3 = M1np
+
+
+    tensor1 = transformINet(tensor1).unsqueeze(0).cuda()
+    tensor2 = transformINet(tensor2).unsqueeze(0).cuda()
+    tensor3 = torch.from_numpy(tensor3).unsqueeze(0).type(torch.FloatTensor).cuda()
+
+    return I1, I2, tensor1, tensor2, tensor3
+
+def forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3):
+    with torch.no_grad() : 
+        feat1 = backbone( tensor1 ) ## feature
+        feat1 = F.normalize(feat1, dim=1) ## l2 normalization
+        feat2 = backbone( tensor2 ) ## features 
+        feat2 = F.normalize(feat2, dim=1) ## l2 normalization
+
+        fmask = backbone(tensor3.unsqueeze(0).repeat(1, 3, 1, 1))
+        fmask = F.normalize(fmask, dim=1)
+
+        out1, out2, out3 = netEncoder(feat1, feat2, fmask) ## predictions
+        m1_final, m2_final, m3_final = out1[0, 2].cpu().numpy(), out2[0, 2].cpu().numpy(), out3.item()
+    
+    return m1_final, m2_final, m3_final
+
+
+def load_frame(path, frame_idx, ret_size=False):
+    img = cv2.imread(os.path.join(path, f'{frame_idx}.jpg'))[..., ::-1]
+    orig_size = img.shape[:-1]
+    #print(orig_size)
+    img = Image.fromarray(reshape_img_war(img))
+    if ret_size:
+        return img, orig_size
+    return img
+
+def egoexo(backbone, netEncoder, annotations, ego, exo, obj, take, anno_path, pred_json):
+
+    pred_json['masks'][obj][f'{ego}_{exo}'] = {}
+    for idx in annotations['masks'][obj][ego].keys():
+
+        ego_frame = load_frame(path=f'{anno_path}/{take}/{ego}/', frame_idx=idx)
+        ego_mask = mask_utils.decode(annotations['masks'][obj][ego][idx])
+        ego_mask = reshape_img_war(ego_mask)
+
+        exo_frame, exo_size = load_frame(path=f'{anno_path}/{take}/{exo}/', frame_idx=idx, ret_size=True)
+
+        Ix, Iy, tensor1, tensor2, tensor3 = get_tensors(ego_frame, exo_frame, ego_mask)
+        mx, my, confidence = forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3)
+
+        y_step = (my > MASKThresh)
+        y_step = utils_new.remove_pad(y_step, orig_size=exo_size)
+        #print(y_step.shape)
+
+        exo_pred = mask_utils.encode(np.asfortranarray(y_step.astype(np.uint8)))
+        exo_pred['counts'] = exo_pred['counts'].decode('ascii')
+        pred_json['masks'][obj][f'{ego}_{exo}'][idx] = {'pred_mask': exo_pred, 'confidence': confidence}
+
+def exoego(backbone, netEncoder, annotations, ego, exo, obj, take, anno_path, pred_json):
+
+    pred_json['masks'][obj][f'{exo}_{ego}'] = {}
+    for idx in annotations['masks'][obj][exo].keys():
+
+        exo_frame = load_frame(path=f'{anno_path}/{take}/{exo}/', frame_idx=idx)
+        exo_mask = mask_utils.decode(annotations['masks'][obj][exo][idx])
+        exo_mask = reshape_img_war(exo_mask)
+
+        ego_frame = load_frame(path=f'{anno_path}/{take}/{ego}/', frame_idx=idx)
+        # print(ego_frame.size)
+        if ego_frame.size != (480, 480):
+            continue
+
+        Ix, Iy, tensor1, tensor2, tensor3 = get_tensors(exo_frame, ego_frame, exo_mask)
+        mx, my, confidence = forward_pass(backbone, netEncoder, tensor1, tensor2, tensor3)
+
+        y_step = (my > MASKThresh)
+
+        ego_pred = mask_utils.encode(np.asfortranarray(y_step.astype(np.uint8)))
+        ego_pred['counts'] = ego_pred['counts'].decode('ascii')
+        pred_json['masks'][obj][f'{exo}_{ego}'][idx] = {'pred_mask': ego_pred, 'confidence': confidence}
+
+def main(model_path, takes, anno_path, out_path, setting='ego-exo', save_inter=False):
+
+    print('TOTAL TAKES: ', len(takes))
+
+    resume_path = '/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/model/moco_v2_800ep_pretrain_torchvision.pth.tar'
+    param = torch.load(resume_path)['model']
+    new_param = {}
+    for key in param.keys():
+        if 'fc' in key:
+            continue
+        new_param[key] = param[key]
+
+    backbone = models.resnet50(pretrained=False)
+    backbone.load_state_dict(new_param, strict=False)
+    resnet_feature_layers = ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']
+    resnet_module_list = [getattr(backbone, l) for l in resnet_feature_layers]
+    last_layer_idx = resnet_feature_layers.index('layer3')
+    backbone = torch.nn.Sequential(*resnet_module_list[:last_layer_idx + 1])
+    feat_dim = 1024
+    backbone.cuda()
+
+    ## model
+    netEncoder = transformer.TransEncoder(feat_dim,
+                                        pos_weight=0.1,
+                                        feat_weight=1,
+                                        dropout=0.1,
+                                        activation="relu",
+                                        mode="small",
+                                        layer_type=['I', 'C', 'I', 'C', 'I', 'N'],
+                                        drop_feat=0.1)
+
+    netEncoder.cuda()
+    
+    results = {}
+    for take in tqdm.tqdm(takes_filter):
+
+        with open(f'{anno_path}/{take}/annotation.json', 'r') as fp:
+            annotations = json.load(fp)
+
+        pred_json = {'masks': {}, 'subsample_idx': annotations['subsample_idx']}
+
+        for obj in annotations['masks']:
+
+            pred_json['masks'][obj] = {}
+
+            cams = annotations['masks'][obj].keys()
+
+            exo_cams = [x for x in cams if 'aria' not in x]
+            ego_cams = [x for x in cams if 'aria' in x]
+
+            for ego in ego_cams:
+                for exo in exo_cams:
+                    # ego -> exo
+                    if setting == 'ego-exo':
+                        egoexo(backbone=backbone, netEncoder=netEncoder, annotations=annotations,
+                                ego=ego, exo=exo, obj=obj, take=take, anno_path=anno_path, pred_json=pred_json)
+                    elif setting == 'exo-ego':
+                        exoego(backbone=backbone, netEncoder=netEncoder, annotations=annotations,
+                                ego=ego, exo=exo, obj=obj, take=take, anno_path=anno_path, pred_json=pred_json)
+                    else:
+                        raise Exception(f"Setting {setting} not recognized.")
+
+        results[take] = pred_json
+
+        if save_inter:
+            os.makedirs(f'{out_path}/{take}', exist_ok=True)
+            with open(f'{out_path}/{take}/pred_annotations.json', 'w') as fp:
+                json.dump(pred_json, fp)
+
+    return results
+
+if __name__ == '__main__':
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--ckpt_path', type=str, required=True)
+    parser.add_argument('--data_path', type=str, required=True)
+    parser.add_argument('--splits_path', type=str, required=True, help="Path to json of take splits")
+    parser.add_argument('--split', type=str, required=True, help="Split to evaluate on")
+    parser.add_argument('--out_path', type=str, required=True)
+    parser.add_argument('--setting', required=True, choices=['ego-exo', 'exo-ego'], help="ego-exo or exo-ego")
+    parser.add_argument('--save_inter', action='store_true', help="Store intermediate take wise results")
+
+    args = parser.parse_args()
+
+    with open(args.splits_path, "r") as fp:
+        splits = json.load(fp)
+    
+    results = main(args.ckpt_path,
+                splits[args.split],
+                args.data_path,
+                args.out_path,
+                setting=args.setting,
+                save_inter=args.save_inter)
+
+    os.makedirs(args.out_path, exist_ok=True)
+    with open(f"{args.out_path}/{args.setting}_{args.split}_results.json", "w") as fp:
+        json.dump({args.setting: {'results': results}}, fp)

correspondence/SegSwap/input.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:009a7db960194af52e44f75b7812baf1525386628acd763adbefa980d1937070
+size 75670357

correspondence/SegSwap/model/convert_torchvision.py

@@ -0,0 +1,25 @@
+import sys
+import torch
+if __name__ == "__main__":
+    input = sys.argv[1]
+    output = sys.argv[2]
+    obj = torch.load(input, map_location="cpu")
+    obj = obj["state_dict"]
+    newmodel = {}
+    for k, v in obj.items():
+        if not k.startswith("module.encoder_q."):
+            continue
+        old_k = k
+        k = k.replace("module.encoder_q.", "")
+        # if "layer" not in k:
+        #     k = "stem." + k
+        # for t in [1, 2, 3, 4]:
+        #     k = k.replace("layer{}".format(t), "res{}".format(t + 1))
+        # for t in [1, 2, 3]:
+        #     k = k.replace("bn{}".format(t), "conv{}.norm".format(t))
+        # k = k.replace("downsample.0", "shortcut")
+        # k = k.replace("downsample.1", "shortcut.norm")
+        print(old_k, "->", k)
+        newmodel[k] = v
+    state = {"model": newmodel, "__author__": "MOCO"}
+    torch.save(state, output)

correspondence/SegSwap/model/download_model.sh

@@ -0,0 +1,6 @@
+wget https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v2_800ep/moco_v2_800ep_pretrain.pth.tar
+
+python convert_torchvision.py moco_v2_800ep_pretrain.pth.tar moco_v2_800ep_pretrain_torchvision.pth.tar
+
+rm moco_v2_800ep_pretrain.pth.tar
+

correspondence/SegSwap/model/environment.yml

@@ -0,0 +1,145 @@
+name: segswap
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1
+  - _openmp_mutex=5.1
+  - asttokens=2.2.1
+  - backcall=0.2.0
+  - backports=1.0
+  - backports.functools_lru_cache=1.6.5
+  - bzip2=1.0.8
+  - ca-certificates=2023.5.7
+  - decorator=5.1.1
+  - entrypoints=0.4
+  - executing=1.2.0
+  - ipykernel=6.15.0
+  - ipython=8.14.0
+  - jedi=0.18.2
+  - jupyter_client=7.3.4
+  - jupyter_core=5.3.1
+  - ld_impl_linux-64=2.38
+  - libffi=3.4.4
+  - libgcc-ng=11.2.0
+  - libgomp=11.2.0
+  - libsodium=1.0.18
+  - libstdcxx-ng=11.2.0
+  - libuuid=1.41.5
+  - matplotlib-inline=0.1.6
+  - ncurses=6.4
+  - nest-asyncio=1.5.6
+  - openssl=3.0.9
+  - packaging=23.1
+  - parso=0.8.3
+  - pexpect=4.8.0
+  - pickleshare=0.7.5
+  - platformdirs=3.8.0
+  - prompt-toolkit=3.0.38
+  - prompt_toolkit=3.0.38
+  - ptyprocess=0.7.0
+  - pure_eval=0.2.2
+  - pygments=2.15.1
+  - python=3.10.11
+  - python-dateutil=2.8.2
+  - python_abi=3.10
+  - readline=8.2
+  - six=1.16.0
+  - sqlite=3.41.2
+  - stack_data=0.6.2
+  - tk=8.6.12
+  - traitlets=5.9.0
+  - typing-extensions=4.6.3
+  - typing_extensions=4.6.3
+  - wcwidth=0.2.6
+  - xz=5.4.2
+  - zeromq=4.3.4
+  - zlib=1.2.13
+  - pip:
+    - absl-py==1.4.0
+    - appdirs==1.4.4
+    - cachetools==5.3.1
+    - certifi==2023.5.7
+    - charset-normalizer==3.1.0
+    - click==8.1.4
+    - cmake==3.26.4
+    - contourpy==1.1.0
+    - cycler==0.11.0
+    - debugpy==1.5.1
+    - decord==0.6.0
+    - docker-pycreds==0.4.0
+    - filelock==3.12.2
+    - fonttools==4.40.0
+    - gitdb==4.0.10
+    - gitpython==3.1.31
+    - google-auth==2.22.0
+    - google-auth-oauthlib==1.0.0
+    - grpcio==1.56.2
+    - idna==3.4
+    - imageio==2.31.5
+    - jinja2==3.1.2
+    - joblib==1.2.0
+    - jupyter-core==5.3.1
+    - kiwisolver==1.4.4
+    - kornia==0.6.12
+    - lazy-loader==0.3
+    - lit==16.0.6
+    - markdown==3.4.3
+    - markupsafe==2.1.3
+    - matplotlib==3.7.1
+    - mpmath==1.3.0
+    - natsort==8.4.0
+    - networkx==3.1
+    - numpy==1.25.0
+    - nvidia-cublas-cu11==11.10.3.66
+    - nvidia-cuda-cupti-cu11==11.7.101
+    - nvidia-cuda-nvrtc-cu11==11.7.99
+    - nvidia-cuda-runtime-cu11==11.7.99
+    - nvidia-cudnn-cu11==8.5.0.96
+    - nvidia-cufft-cu11==10.9.0.58
+    - nvidia-curand-cu11==10.2.10.91
+    - nvidia-cusolver-cu11==11.4.0.1
+    - nvidia-cusparse-cu11==11.7.4.91
+    - nvidia-nccl-cu11==2.14.3
+    - nvidia-nvtx-cu11==11.7.91
+    - oauthlib==3.2.2
+    - opencv-python==4.7.0.72
+    - pandas==2.0.3
+    - pathtools==0.1.2
+    - pillow==9.5.0
+    - pip==23.1.2
+    - protobuf==4.23.4
+    - psutil==5.9.0
+    - pyasn1==0.5.0
+    - pyasn1-modules==0.3.0
+    - pycocotools==2.0.6
+    - pyparsing==3.1.0
+    - pytz==2023.3
+    - pyyaml==6.0
+    - pyzmq==25.1.0
+    - requests==2.31.0
+    - requests-oauthlib==1.3.1
+    - rsa==4.9
+    - scikit-image==0.22.0
+    - scikit-learn==1.2.2
+    - scipy==1.11.0
+    - sentry-sdk==1.27.1
+    - setproctitle==1.3.2
+    - setuptools==67.8.0
+    - smmap==5.0.0
+    - sympy==1.12
+    - tensorboard==2.13.0
+    - tensorboard-data-server==0.7.1
+    - tensorboardx==2.6.1
+    - threadpoolctl==3.1.0
+    - tifffile==2023.9.26
+    - torch==2.0.1
+    - torchvision==0.15.2
+    - tornado==6.1
+    - tqdm==4.65.0
+    - triton==2.0.0
+    - tzdata==2023.3
+    - urllib3==1.26.16
+    - wandb==0.15.5
+    - werkzeug==2.3.6
+    - wheel==0.38.4

correspondence/SegSwap/model/moco_v2_800ep_pretrain_torchvision.pth.tar

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+oid sha256:d0a05442306e2a62f5dd0a00cf7cf8ed18121de291c89e255820c5f55f0c94d7
+size 112184505

correspondence/SegSwap/model/model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:299e98583ff885764bdeebb388f8402736a0d552c7e0997a57012dcced3c6e11
+size 77583173

correspondence/SegSwap/model/transformer.py

@@ -0,0 +1,355 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+import math
+
+### --- Positional encoding--- ###
+### --- Borrowed from Detr--- ###
+class PositionEncodingSine2D(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=True, scale=None):
+        super(PositionEncodingSine2D, self).__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, x, isTarget = False):
+        '''
+        input x: B, C, H, W
+        return pos: B, C, H, W
+        
+        '''
+        not_mask = torch.ones(x.size()[0], x.size()[2], x.size()[3]).to(x.device)
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        
+        if self.normalize:
+            eps = 1e-6
+            ## no diff between source and target
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+class EncoderLayerInnerAttention(nn.Module):
+    """
+    Transformer encoder with all paramters
+    """
+    def __init__(self, d_model, nhead, dim_feedforward, dropout, activation, pos_weight, feat_weight):
+        super(EncoderLayerInnerAttention, self).__init__()
+        
+        
+        self.pos_weight = pos_weight
+        self.feat_weight = feat_weight
+        self.inner_encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout, activation = activation)
+        self.posEncoder = PositionEncodingSine2D(d_model // 2)
+        
+        self.cross_encoder_layer = EncoderLayerCrossAttention(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward,
+                                                                dropout=dropout, activation=activation)
+        
+    def forward(self, x, y, featmask, x_mask = None, y_mask = None):
+        '''
+        input x: B, C, H, W
+        input y: B, C, H, W
+        input x_mask: B, 1, H, W, mask == True will be ignored
+        input y_mask: B, 1, H, W, mask == True will be ignored
+        '''
+        
+        # do cross attention on x and featmask
+        x = self.cross_encoder_layer(x, featmask, None)[0]
+
+        bx, cx, hx, wx = x.size()
+        
+        by, cy, hy, wy = y.size()
+        
+        posx = self.posEncoder(x)
+        posy = self.posEncoder(y)
+        
+        
+        featx = self.feat_weight * x + self.pos_weight * posx
+        featy = self.feat_weight * y + self.pos_weight * posy
+        
+        
+        ## input of transformer should be : seq_len * batch_size * feat_dim 
+        featx = featx.flatten(2).permute(2, 0, 1) 
+        featy = featy.flatten(2).permute(2, 0, 1)
+        x_mask = x_mask.flatten(2).squeeze(1) if x_mask is not None else torch.cuda.BoolTensor(bx, hx * wx).fill_(False)
+        y_mask = y_mask.flatten(2).squeeze(1) if y_mask is not None else torch.cuda.BoolTensor(by, hy * wy).fill_(False)
+        
+        ## input of transformer: (seq_len*2) * batch_size * feat_dim
+        len_seq_x, len_seq_y = featx.size()[0], featy.size()[0]
+        
+        output = torch.cat([featx, featy], dim=0)
+        src_key_padding_mask = torch.cat((x_mask, y_mask), dim=1)
+        with torch.no_grad() : 
+            src_mask = torch.cuda.BoolTensor(hx * wx + hy * wy, hx * wx + hy * wy).fill_(True)
+            src_mask[:hx * wx, :hx * wx] = False
+            src_mask[hx * wx :, hx * wx:] = False
+        
+        output = self.inner_encoder_layer(output, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask)
+
+        outx, outy = output.narrow(0, 0, len_seq_x), output.narrow(0, len_seq_x, len_seq_y)  
+        outx, outy = outx.permute(1, 2, 0).view(bx, cx, hx, wx), outy.permute(1, 2, 0).view(by, cy, hy, wy)
+        x_mask, y_mask = x_mask.view(bx, 1, hx, wx), y_mask.view(bx, 1, hy, wy)
+        
+        return  outx, outy, x_mask, y_mask
+    
+class EncoderLayerCrossAttention(nn.Module):
+    """
+    Transformer encoder with all paramters
+    """
+    def __init__(self, d_model, nhead, dim_feedforward, dropout, activation):
+        super(EncoderLayerCrossAttention, self).__init__()
+        
+        self.cross_encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout, activation = activation)
+        
+    def forward(self, featx, featy, featmask, x_mask = None, y_mask = None):
+        '''
+        input x: B, C, H, W
+        input y: B, C, H, W
+        input x_mask: B, 1, H, W, mask == True will be ignored
+        input y_mask: B, 1, H, W, mask == True will be ignored
+        '''
+        
+        bx, cx, hx, wx = featx.size()
+        by, cy, hy, wy = featy.size()
+        
+        ## input of transformer should be : seq_len * batch_size * feat_dim 
+        featx = featx.flatten(2).permute(2, 0, 1) 
+        featy = featy.flatten(2).permute(2, 0, 1)
+        x_mask = x_mask.flatten(2).squeeze(1) if x_mask is not None else torch.cuda.BoolTensor(bx, hx * wx).fill_(False)
+        y_mask = y_mask.flatten(2).squeeze(1) if y_mask is not None else torch.cuda.BoolTensor(by, hy * wy).fill_(False)
+        
+        ## input of transformer: (seq_len*2) * batch_size * feat_dim
+        len_seq_x, len_seq_y = featx.size()[0], featy.size()[0]
+        
+        output = torch.cat([featx, featy], dim=0)
+        src_key_padding_mask = torch.cat((x_mask, y_mask), dim=1)
+        with torch.no_grad() : 
+            src_mask = torch.cuda.BoolTensor(hx * wx + hy * wy, hx * wx + hy * wy).fill_(False)
+            src_mask[:hx * wx, :hx * wx] = True
+            src_mask[hx * wx :, hx * wx:] = True
+            
+        output = self.cross_encoder_layer(output, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask)
+        
+        outx, outy = output.narrow(0, 0, len_seq_x), output.narrow(0, len_seq_x, len_seq_y)  
+        outx, outy = outx.permute(1, 2, 0).view(bx, cx, hx, wx), outy.permute(1, 2, 0).view(by, cy, hy, wy)
+        x_mask, y_mask = x_mask.view(bx, 1, hx, wx), y_mask.view(bx, 1, hy, wy)
+        
+        return  outx, outy, x_mask, y_mask
+    
+class EncoderLayerEmpty(nn.Module):
+    """
+    Transformer encoder with all paramters
+    """
+    def __init__(self):
+        super(EncoderLayerEmpty, self).__init__()
+        
+    def forward(self, featx, featy, featmask, x_mask = None, y_mask = None):
+        '''
+        input x: B, C, H, W
+        input y: B, C, H, W
+        input x_mask: B, 1, H, W, mask == True will be ignored
+        input y_mask: B, 1, H, W, mask == True will be ignored
+        '''
+        return  featx, featy, x_mask, y_mask
+    
+class EncoderLayerBlock(nn.Module):
+    """
+    Transformer encoder with all paramters
+    """
+    def __init__(self, d_model, nhead, dim_feedforward, dropout, activation, pos_weight, feat_weight, layer_type) :
+        super(EncoderLayerBlock, self).__init__()
+        
+        cross_encoder_layer = EncoderLayerCrossAttention(d_model, nhead, dim_feedforward, dropout, activation)
+        att_encoder_layer = EncoderLayerInnerAttention(d_model, nhead, dim_feedforward, dropout, activation, pos_weight, feat_weight)
+        
+        if layer_type[0] == 'C' :
+            self.layer1 = cross_encoder_layer 
+        elif layer_type[0] == 'I' :
+            self.layer1 = att_encoder_layer 
+        elif layer_type[0] == 'N' :
+            self.layer1 = EncoderLayerEmpty()
+        
+        if layer_type[1] == 'C' :
+            self.layer2 = cross_encoder_layer 
+        elif layer_type[1] == 'I' :
+            self.layer2 = att_encoder_layer 
+        elif layer_type[1] == 'N' :
+            self.layer2 = EncoderLayerEmpty()
+
+    def forward(self, featx, featy, featmask, x_mask = None, y_mask = None):
+        '''
+        input x: B, C, H, W
+        input y: B, C, H, W
+        input x_mask: B, 1, H, W, mask == True will be ignored
+        input y_mask: B, 1, H, W, mask == True will be ignored
+        '''
+        
+        featx, featy, x_mask, y_mask = self.layer1(featx, featy, featmask, x_mask, y_mask)
+        featx, featy, x_mask, y_mask = self.layer2(featx, featy, featmask, x_mask, y_mask)
+
+        return  featx, featy, x_mask, y_mask
+    
+### --- Transformer Encoder --- ###
+class Decoder(nn.Module):
+
+    def __init__(self, in_channels=256, out_channels=3):
+        super(Decoder, self).__init__()
+
+        self.deconv1 = nn.ConvTranspose2d(in_channels, 128, 2, stride=2) # 60
+        self.relu1 = nn.ReLU()
+
+        self.deconv2 = nn.ConvTranspose2d(128, out_channels, 2, stride=2) # 120
+
+    def forward(self, x):
+
+        x = self.deconv1(x)
+        x = self.relu1(x)
+
+        x = self.deconv2(x)
+
+        x = F.interpolate(x, scale_factor=4, mode='bilinear', align_corners=False)
+
+        return x
+    
+class ClsBranch(nn.Module):
+    # branch to predict if the object exists or not.
+    def __init__(self, in_dim):
+        super(ClsBranch, self).__init__()
+
+        self.conv = nn.Conv2d(in_dim, 1, 3)
+        self.relu = nn.ReLU()
+
+        self.mlp = nn.Sequential(*[nn.Linear(28*28, 32), 
+                                    nn.ReLU(), 
+                                    nn.Linear(32, 1), 
+                                    nn.Sigmoid()])
+        
+    def forward(self, x):
+
+        x = self.conv(x)
+        x = self.relu(x)
+        x = torch.flatten(x, start_dim=1)
+        x = self.mlp(x)
+        return x
+        
+class Encoder(nn.Module):
+    """
+    Transformer encoder with all paramters
+    """
+    def __init__(self, feat_dim, pos_weight = 0.1, feat_weight=1, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048, dropout=0.1, activation='relu', layer_type = ['I', 'C', 'I', 'C', 'I', 'C'], drop_feat = 0.1):
+        super(Encoder, self).__init__()
+        
+        self.num_layers = num_layers
+        self.feat_proj = nn.Conv2d(feat_dim, d_model, kernel_size=1)
+        self.drop_feat = nn.Dropout2d(p=drop_feat)
+        self.encoder_blocks = nn.ModuleList([EncoderLayerBlock(d_model, nhead, dim_feedforward, dropout, activation, pos_weight, feat_weight, layer_type[i * 2 : i * 2 + 2]) for i in range(num_layers)])
+
+        self.decoder = Decoder(d_model, 3)
+        self.cls_branch = ClsBranch(in_dim=256)
+        self.sigmoid = nn.Sigmoid()
+        self.eps = 1e-7
+        
+        
+    def forward(self, x, y, fmask, x_mask = None, y_mask = None):
+        '''
+        input x: B, C, H, W
+        input y: B, C, H, W
+        input x_mask: B, 1, H, W, mask == True will be ignored
+        input y_mask: B, 1, H, W, mask == True will be ignored
+        '''
+        featx = self.feat_proj (x)
+        featx = self.drop_feat(featx)
+        
+        bx, cx, hx, wx = featx.size()
+        
+        featy = self.feat_proj (y)
+        featy = self.drop_feat(featy)
+        
+        by, cy, hy, wy = featy.size()
+
+        featmask = self.feat_proj(fmask)
+
+        for i in range(self.num_layers) : 
+            featx, featy, x_mask, y_mask = self.encoder_blocks[i](featx, featy, featmask, x_mask, y_mask)
+        
+        out_cls = self.cls_branch(featy)
+        outx = self.sigmoid(self.decoder(featx))
+        outy = self.sigmoid(self.decoder(featy))
+
+        outx = torch.clamp(outx, min=self.eps, max=1-self.eps)
+        outy = torch.clamp(outy, min=self.eps, max=1-self.eps)
+        
+        return  outx, outy, out_cls
+    
+### --- Transformer Encoder --- ###
+class TransEncoder(nn.Module):
+    """
+    Transformer encoder: small and large variants
+    """
+    def __init__(self, feat_dim=1024, pos_weight = 0.1, feat_weight = 1, dropout=0.1, activation='relu', mode='small', layer_type=['I', 'C', 'I', 'C', 'I', 'N'], drop_feat=0.1):
+        super(TransEncoder, self).__init__()
+        
+        if mode == 'tiny' : 
+            d_model=128
+            nhead=2
+            num_layers=3
+            dim_feedforward=256
+            
+        elif mode == 'small' : 
+            d_model=256
+            nhead=2
+            num_layers=3
+            dim_feedforward=256
+            
+        elif mode == 'base' : 
+            d_model=512
+            nhead=8
+            num_layers=3
+            dim_feedforward=2048
+            
+        elif mode == 'large' : 
+            d_model=512
+            nhead=8
+            num_layers=6
+            dim_feedforward=2048
+            
+        self.net = Encoder(feat_dim, pos_weight, feat_weight, d_model, nhead, num_layers, dim_feedforward, dropout, activation, layer_type, drop_feat)
+        
+    def forward(self, x, y, fmask, x_mask = None, y_mask = None):
+        '''
+        input x: B, C, H, W
+        input y: B, C, H, W
+        
+        '''
+        outx, outy, out_cls = self.net(x, y, fmask, x_mask, y_mask)
+
+        return  outx, outy, out_cls
+    
+if __name__ == '__main__' : 
+    
+    feat_dim = 256
+    mode = 'small'
+    x = torch.cuda.FloatTensor(2, feat_dim, 10, 10)
+    x_mask = torch.cuda.BoolTensor(2, 1, 10, 10)
+    
+    net = TransEncoder()
+    
+    print (net)

correspondence/SegSwap/process_data.sh

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+#!/bin/bash
+#SBATCH --job-name=process_data13       # Job name
+#SBATCH --nodes=1               # Request 1 node
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=16
+#SBATCH --mem-per-cpu=8G
+#SBATCH --time=24:00:00         # Job timeout
+#SBATCH --output=process_test_data_20250512_check.log     # Redirect stdout to a log file
+#SBATCH --nodelist=gcpcpu-eu-1
+
+
+
+cd /home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap
+
+srun --nodes "$SLURM_NNODES" --ntasks-per-node 1 -- \
+mkenv -f /home/yuqian_fu/environment.yml -- \
+sh -c "
+
+python data/process_data_new.py --takepath /work/yuqian_fu/Ego/egoexo4d_keystep/takes --annotationpath /work/yuqian_fu/Ego/New_annotations/annotations/relations_test.json --split_path /home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/split.json --split test --outputpath /work/yuqian_fu/Ego/data_segswap_test_20250512
+
+"
+
+
+

correspondence/SegSwap/process_test_data_20250507.log

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+
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+slurmstepd: error: Detected 1 oom_kill event in StepId=221267.0. Some of the step tasks have been OOM Killed.
+srun: error: gcpl4-eu-0: task 0: Out Of Memory

correspondence/SegSwap/process_test_data_20250508_new.log

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  6%|▋        slurmstepd: error: *** JOB 221511 ON gcpl4-eu-0 CANCELLED AT 2025-05-08T09:51:37 ***
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+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.

correspondence/SegSwap/process_test_data_20250508_new666.log

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+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.

correspondence/SegSwap/process_test_data_20250508_new888.log

@@ -0,0 +1,6 @@
+Traceback (most recent call last):
+  File "/home/yuqian_fu/Projects/ego-exo4d-relation/correspondence/SegSwap/data/process_data_new.py", line 123, in <module>
+    with open(args.annotationpath, "r") as f:
+         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+FileNotFoundError: [Errno 2] No such file or directory: '/work/yuqian_fu/Ego/New_annotations/annotations/relations_test.json'
+srun: error: msp3-1: task 0: Exited with exit code 1

correspondence/SegSwap/process_test_data_20250508_new999.log

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  9%|▉         | 27/295 [1:03:48<9:52:42, 132.70s/it] slurmstepd: error: *** STEP 221704.0 ON gcpl4-eu-0 CANCELLED AT 2025-05-08T14:25:01 ***
+slurmstepd: error: *** JOB 221704 ON gcpl4-eu-0 CANCELLED AT 2025-05-08T14:25:01 ***
+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.

correspondence/SegSwap/process_test_data_20250511_new.log

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+ 105.56s/it]
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 16%|█▌        | 47/295 [1:03:41<4:18:35, 62.56s/it]slurmstepd: error: *** STEP 227599.0 ON gcp-eu-1 CANCELLED AT 2025-05-11T16:37:21 ***
+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.

correspondence/SegSwap/process_test_data_20250512.log

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+Total time: 31935.866154909134 seconds