Closed Deaddawn closed 1 year ago
Deaddawn
commented
1 year ago I have solved this problem by using codes: **use_gpu = lambda x=True: torch.set_default_tensor_type(torch.cuda.FloatTensor if torch.cuda.is_available() and x else torch.FloatTensor)
use_gpu()** in some of the scripts to set gpu settings globally. Anyone who may encounter this problem can try this. Besides, still wanna paper crews to check this problem. This shouldn't be on your official repository.
yunfanjiang
commented
1 year ago Thank you for your interest in our project. The script was tested fine upon initial release. We will test again later to see if we could reproduce the same error and fix if necessary.
LRQ577
commented
10 months ago `(vima) li@li:~/VIMA-main$ python3 scripts/example.py --ckpt=200M.ckpt --partition=placement_generalization --task=follow_order pybullet build time: Nov 28 2023 23:52:03 [INFO] 17 tasks loaded Some weights of the model checkpoint at t5-base were not used when initializing T5EncoderModel: ['decoder.block.8.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.1.EncDecAttention.o.weight', 'decoder.block.10.layer.0.SelfAttention.o.weight', 'decoder.block.8.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.3.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.0.SelfAttention.q.weight', 'decoder.block.8.layer.2.DenseReluDense.wo.weight', 'decoder.block.7.layer.1.EncDecAttention.v.weight', 'decoder.block.11.layer.0.layer_norm.weight', 'decoder.block.4.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.2.DenseReluDense.wo.weight', 'decoder.block.5.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.1.layer_norm.weight', 'decoder.block.3.layer.0.layer_norm.weight', 'decoder.block.4.layer.0.layer_norm.weight', 'decoder.block.2.layer.1.EncDecAttention.q.weight', 'decoder.block.9.layer.0.SelfAttention.v.weight', 'decoder.block.4.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.9.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.1.EncDecAttention.k.weight', 'decoder.block.11.layer.1.EncDecAttention.k.weight', 'decoder.block.7.layer.0.layer_norm.weight', 'decoder.block.9.layer.1.layer_norm.weight', 'decoder.block.11.layer.2.layer_norm.weight', 'decoder.block.3.layer.1.EncDecAttention.k.weight', 'decoder.block.6.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.2.layer_norm.weight', 'decoder.block.5.layer.1.layer_norm.weight', 'decoder.block.9.layer.1.EncDecAttention.q.weight', 'decoder.block.4.layer.2.DenseReluDense.wo.weight', 'decoder.block.6.layer.1.EncDecAttention.v.weight', 'decoder.block.5.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.0.layer_norm.weight', 'decoder.block.4.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.0.SelfAttention.v.weight', 'decoder.block.8.layer.2.layer_norm.weight', 'decoder.block.11.layer.2.DenseReluDense.wi.weight', 'decoder.block.7.layer.0.SelfAttention.q.weight', 'decoder.block.5.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.2.DenseReluDense.wo.weight', 'decoder.block.4.layer.0.SelfAttention.o.weight', 'decoder.block.11.layer.2.DenseReluDense.wo.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.6.layer.0.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.9.layer.1.EncDecAttention.o.weight', 'decoder.block.7.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.0.SelfAttention.o.weight', 'decoder.block.2.layer.1.EncDecAttention.v.weight', 'decoder.block.5.layer.2.DenseReluDense.wi.weight', 'decoder.block.6.layer.0.SelfAttention.q.weight', 'decoder.block.7.layer.2.DenseReluDense.wo.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.7.layer.2.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'decoder.block.4.layer.0.SelfAttention.k.weight', 'decoder.block.10.layer.2.layer_norm.weight', 'decoder.block.11.layer.1.EncDecAttention.v.weight', 'decoder.block.9.layer.2.layer_norm.weight', 'decoder.block.2.layer.0.SelfAttention.k.weight', 'decoder.final_layer_norm.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'decoder.block.6.layer.1.EncDecAttention.q.weight', 'decoder.block.6.layer.1.layer_norm.weight', 'decoder.block.3.layer.0.SelfAttention.q.weight', 'decoder.block.10.layer.0.SelfAttention.q.weight', 'decoder.block.2.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'decoder.block.6.layer.2.DenseReluDense.wi.weight', 'decoder.block.4.layer.2.DenseReluDense.wi.weight', 'decoder.block.8.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.3.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.3.layer.1.layer_norm.weight', 'decoder.block.7.layer.0.SelfAttention.k.weight', 'decoder.block.5.layer.1.EncDecAttention.v.weight', 'decoder.block.6.layer.0.SelfAttention.v.weight', 'decoder.block.9.layer.0.SelfAttention.q.weight', 'decoder.block.5.layer.0.SelfAttention.k.weight', 'decoder.block.8.layer.1.EncDecAttention.q.weight', 'decoder.block.4.layer.0.SelfAttention.q.weight', 'decoder.block.6.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.4.layer.2.layer_norm.weight', 'decoder.block.11.layer.1.layer_norm.weight', 'decoder.block.11.layer.1.EncDecAttention.q.weight', 'decoder.block.8.layer.0.SelfAttention.v.weight', 'decoder.block.7.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.8.layer.0.SelfAttention.o.weight', 'decoder.block.9.layer.2.DenseReluDense.wi.weight', 'decoder.block.2.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.2.layer_norm.weight', 'decoder.block.6.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.0.SelfAttention.q.weight', 'decoder.block.2.layer.0.SelfAttention.o.weight', 'decoder.block.4.layer.1.layer_norm.weight', 'decoder.block.9.layer.1.EncDecAttention.v.weight', 'decoder.block.11.layer.0.SelfAttention.v.weight', 'decoder.block.11.layer.0.SelfAttention.o.weight', 'decoder.block.9.layer.0.SelfAttention.o.weight', 'decoder.block.7.layer.2.DenseReluDense.wi.weight', 'decoder.block.10.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.1.EncDecAttention.q.weight', 'decoder.block.3.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.6.layer.0.SelfAttention.o.weight', 'decoder.block.9.layer.0.SelfAttention.k.weight', 'decoder.block.10.layer.1.layer_norm.weight', 'decoder.block.10.layer.2.DenseReluDense.wi.weight', 'decoder.block.2.layer.0.layer_norm.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'decoder.block.7.layer.0.SelfAttention.o.weight', 'decoder.block.5.layer.0.layer_norm.weight', 'decoder.block.10.layer.2.DenseReluDense.wo.weight', 'decoder.block.8.layer.0.SelfAttention.q.weight', 'decoder.block.2.layer.0.SelfAttention.v.weight', 'decoder.block.9.layer.2.DenseReluDense.wo.weight', 'decoder.block.3.layer.0.SelfAttention.k.weight', 'decoder.block.8.layer.0.SelfAttention.k.weight', 'decoder.block.7.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.4.layer.1.EncDecAttention.q.weight', 'decoder.block.10.layer.1.EncDecAttention.o.weight', 'decoder.block.10.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.11.layer.0.SelfAttention.k.weight', 'decoder.block.6.layer.2.layer_norm.weight', 'decoder.block.4.layer.0.SelfAttention.v.weight', 'decoder.block.2.layer.2.DenseReluDense.wi.weight', 'decoder.block.3.layer.2.DenseReluDense.wi.weight', 'decoder.block.5.layer.2.layer_norm.weight', 'decoder.block.9.layer.0.layer_norm.weight', 'decoder.block.10.layer.1.EncDecAttention.v.weight', 'decoder.block.7.layer.1.layer_norm.weight', 'decoder.block.7.layer.0.SelfAttention.v.weight', 'decoder.block.11.layer.0.SelfAttention.q.weight', 'decoder.block.11.layer.1.EncDecAttention.o.weight', 'decoder.block.8.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'decoder.block.8.layer.0.layer_norm.weight', 'decoder.block.10.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.0.SelfAttention.o.weight', 'decoder.block.6.layer.1.EncDecAttention.o.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.8.layer.1.EncDecAttention.k.weight']
np.bool will be defined as the corresponding NumPy scalar.
view: np.ones((n_objs_prompt[view],), dtype=np.bool)
int args: [Traceback (most recent call last):
File "/home/li/VIMA-main/scripts/example.py", line 507, in np.bool was a deprecated alias for the builtin bool. To avoid this error in existing code, use bool by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.boolhere. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations numActiveThreads = 0 stopping threads Thread with taskId 0 exiting Thread TERMINATED destroy semaphore semaphore destroyed destroy main semaphore main semaphore destroyed finished numActiveThreads = 0 btShutDownExampleBrowser stopping threads Thread with taskId 0 exiting Thread TERMINATED destroy semaphore semaphore destroyed destroy main semaphore main semaphore destroyed We are just using cpu, run python3 scripts/example.py --ckpt=200M.ckpt --partition=placement_generalization --task=follow_order pybullet pop questions like this, and the visual demo just flashed in a secs. Hope for some solution, plz~
LRQ577
commented
10 months ago `(vima) li@li:~/VIMA-main$ python3 scripts/example.py --ckpt=200M.ckpt --partition=placement_generalization --task=follow_order pybullet build time: Nov 28 2023 23:52:03 [INFO] 17 tasks loaded Some weights of the model checkpoint at t5-base were not used when initializing T5EncoderModel: ['decoder.block.8.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.1.EncDecAttention.o.weight', 'decoder.block.10.layer.0.SelfAttention.o.weight', 'decoder.block.8.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.3.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.0.SelfAttention.q.weight', 'decoder.block.8.layer.2.DenseReluDense.wo.weight', 'decoder.block.7.layer.1.EncDecAttention.v.weight', 'decoder.block.11.layer.0.layer_norm.weight', 'decoder.block.4.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.2.DenseReluDense.wo.weight', 'decoder.block.5.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.1.layer_norm.weight', 'decoder.block.3.layer.0.layer_norm.weight', 'decoder.block.4.layer.0.layer_norm.weight', 'decoder.block.2.layer.1.EncDecAttention.q.weight', 'decoder.block.9.layer.0.SelfAttention.v.weight', 'decoder.block.4.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.9.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.1.EncDecAttention.k.weight', 'decoder.block.11.layer.1.EncDecAttention.k.weight', 'decoder.block.7.layer.0.layer_norm.weight', 'decoder.block.9.layer.1.layer_norm.weight', 'decoder.block.11.layer.2.layer_norm.weight', 'decoder.block.3.layer.1.EncDecAttention.k.weight', 'decoder.block.6.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.2.layer_norm.weight', 'decoder.block.5.layer.1.layer_norm.weight', 'decoder.block.9.layer.1.EncDecAttention.q.weight', 'decoder.block.4.layer.2.DenseReluDense.wo.weight', 'decoder.block.6.layer.1.EncDecAttention.v.weight', 'decoder.block.5.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.0.layer_norm.weight', 'decoder.block.4.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.0.SelfAttention.v.weight', 'decoder.block.8.layer.2.layer_norm.weight', 'decoder.block.11.layer.2.DenseReluDense.wi.weight', 'decoder.block.7.layer.0.SelfAttention.q.weight', 'decoder.block.5.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.2.DenseReluDense.wo.weight', 'decoder.block.4.layer.0.SelfAttention.o.weight', 'decoder.block.11.layer.2.DenseReluDense.wo.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.6.layer.0.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 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'decoder.block.9.layer.2.DenseReluDense.wo.weight', 'decoder.block.3.layer.0.SelfAttention.k.weight', 'decoder.block.8.layer.0.SelfAttention.k.weight', 'decoder.block.7.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.4.layer.1.EncDecAttention.q.weight', 'decoder.block.10.layer.1.EncDecAttention.o.weight', 'decoder.block.10.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.11.layer.0.SelfAttention.k.weight', 'decoder.block.6.layer.2.layer_norm.weight', 'decoder.block.4.layer.0.SelfAttention.v.weight', 'decoder.block.2.layer.2.DenseReluDense.wi.weight', 'decoder.block.3.layer.2.DenseReluDense.wi.weight', 'decoder.block.5.layer.2.layer_norm.weight', 'decoder.block.9.layer.0.layer_norm.weight', 'decoder.block.10.layer.1.EncDecAttention.v.weight', 'decoder.block.7.layer.1.layer_norm.weight', 'decoder.block.7.layer.0.SelfAttention.v.weight', 'decoder.block.11.layer.0.SelfAttention.q.weight', 'decoder.block.11.layer.1.EncDecAttention.o.weight', 'decoder.block.8.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'decoder.block.8.layer.0.layer_norm.weight', 'decoder.block.10.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.0.SelfAttention.o.weight', 'decoder.block.6.layer.1.EncDecAttention.o.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.8.layer.1.EncDecAttention.k.weight']
* This IS expected if you are initializing T5EncoderModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). * This IS NOT expected if you are initializing T5EncoderModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of T5EncoderModel were not initialized from the model checkpoint at t5-base and are newly initialized: ['encoder.embed_tokens.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. /home/li/anaconda3/envs/vima/lib/python3.9/site-packages/gym/spaces/box.py:73: UserWarning: WARN: Box bound precision lowered by casting to float32 logger.warn( startThreads creating 1 threads. starting thread 0 started thread 0 argc=2 argv[0] = --unused argv[1] = --start_demo_name=Physics Server ExampleBrowserThreadFunc started X11 functions dynamically loaded using dlopen/dlsym OK! X11 functions dynamically loaded using dlopen/dlsym OK! Creating context Created GL 3.3 context Direct GLX rendering context obtained Making context current GL_VENDOR=NVIDIA Corporation GL_RENDERER=NVIDIA GeForce RTX 4080/PCIe/SSE2 GL_VERSION=3.3.0 NVIDIA 535.146.02 GL_SHADING_LANGUAGE_VERSION=3.30 NVIDIA via Cg compiler pthread_getconcurrency()=0 Version = 3.3.0 NVIDIA 535.146.02 Vendor = NVIDIA Corporation Renderer = NVIDIA GeForce RTX 4080/PCIe/SSE2 b3Printf: Selected demo: Physics Server startThreads creating 1 threads. starting thread 0 started thread 0 MotionThreadFunc thread started text argument:/home/li/VIMABench-main/VimaBench/vima_bench/tasks/assets int args: [ven = NVIDIA Corporation ven = NVIDIA Corporation text argument:/home/li/VIMABench-main/VimaBench/vima_bench/tasks/assets int args: [text argument:/home/li/VIMABench-main/VimaBench/vima_bench/tasks/assets int args: [text argument:/home/li/VIMABench-main/VimaBench/vima_bench/tasks/assets /home/li/VIMA-main/scripts/example.py:337: FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar. view: np.ones((n_objs_prompt[view],), dtype=np.bool) int args: [Traceback (most recent call last): File "/home/li/VIMA-main/scripts/example.py", line 507, in main(arg) File "/home/li/anaconda3/envs/vima/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/home/li/VIMA-main/scripts/example.py", line 114, in main prompt_token_type, word_batch, image_batch = prepare_prompt( File "/home/li/VIMA-main/scripts/example.py", line 336, in prepare_prompt token["mask"] = { File "/home/li/VIMA-main/scripts/example.py", line 337, in view: np.ones((n_objs_prompt[view],), dtype=np.bool) File "/home/li/anaconda3/envs/vima/lib/python3.9/site-packages/numpy/**init**.py", line 324, in **getattr** raise AttributeError(**former_attrs**[attr]) AttributeError: module 'numpy' has no attribute 'bool'. `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations numActiveThreads = 0 stopping threads Thread with taskId 0 exiting Thread TERMINATED destroy semaphore semaphore destroyed destroy main semaphore main semaphore destroyed finished numActiveThreads = 0 btShutDownExampleBrowser stopping threads Thread with taskId 0 exiting Thread TERMINATED destroy semaphore semaphore destroyed destroy main semaphore main semaphore destroyed `We are just using cpu, run
python3 scripts/example.py --ckpt=200M.ckpt --partition=placement_generalization --task=follow_order pybulletpop questions like this, and the visual demo just flashed in a secs. Hope for some solution, plz~
AttributeError: module 'numpy' has no attribute 'bool'.
This error is because the numpy's version is so new.
np.bool has been deprecated/discarded, since numpy 1.20.
Solution : modefy np.bool to bool. I remember this are two places to modefied.
Hi, I am now trying to using gpu to run the demo(it's all good when using only --device=cpu), the following are the error info in my terminal, could you please give me an answer. By the way, my exper settings: GPU3060, ubuntu2004, python3.9, pytorch1.12.1, cuda 11.4. Besides, I have tried to alter the code about cuda and cpu conflict, no results by this time.
error messages:
python scripts/example.py --ckpt=../c --device=cuda pybullet build time: May 20 2022 19:45:31 [INFO] 17 tasks loaded /home/murphy/anaconda3/envs/vima/lib/python3.9/site-packages/gym/spaces/box.py:73: UserWarning: WARN: Box bound precision lowered by casting to float32 logger.warn( startThreads creating 1 threads. starting thread 0 started thread 0 argc=2 argv[0] = --unused argv[1] = --start_demo_name=Physics Server ExampleBrowserThreadFunc started X11 functions dynamically loaded using dlopen/dlsym OK! X11 functions dynamically loaded using dlopen/dlsym OK! Creating context Created GL 3.3 context Direct GLX rendering context obtained Making context current GL_VENDOR=NVIDIA Corporation GL_RENDERER=NVIDIA GeForce RTX 3060 Laptop GPU/PCIe/SSE2 GL_VERSION=3.3.0 NVIDIA 470.199.02 GL_SHADING_LANGUAGE_VERSION=3.30 NVIDIA via Cg compiler pthread_getconcurrency()=0 Version = 3.3.0 NVIDIA 470.199.02 Vendor = NVIDIA Corporation Renderer = NVIDIA GeForce RTX 3060 Laptop GPU/PCIe/SSE2 b3Printf: Selected demo: Physics Server startThreads creating 1 threads. starting thread 0 started thread 0 MotionThreadFunc thread started text argument:/home/murphy/workspace/VIMABench/vima_bench/tasks/assets int args: [ven = NVIDIA Corporation ven = NVIDIA Corporation Traceback (most recent call last): File "/home/murphy/workspace/VIMA/scripts/example.py", line 506, in
main(arg)
File "/home/murphy/anaconda3/envs/vima/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, kwargs)
File "/home/murphy/workspace/VIMA/scripts/example.py", line 118, in main
prompt_tokens, prompt_masks = policy.forward_prompt_assembly(
File "/home/murphy/workspace/VIMA/vima/policy/vima_policy.py", line 163, in forward_prompt_assembly
batch_word_emb = self.prompt_embedding(word_batch)
File "/home/murphy/anaconda3/envs/vima/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, *kwargs)
File "/home/murphy/workspace/VIMA/vima/nn/prompt_encoder/word_embd.py", line 22, in forward
x = self._embed_layer(x)
File "/home/murphy/anaconda3/envs/vima/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(input, kwargs)
File "/home/murphy/anaconda3/envs/vima/lib/python3.9/site-packages/torch/nn/modules/sparse.py", line 158, in forward
return F.embedding(
File "/home/murphy/anaconda3/envs/vima/lib/python3.9/site-packages/torch/nn/functional.py", line 2199, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument index in method wrapper__index_select)
numActiveThreads = 0
stopping threads
Thread with taskId 0 exiting
Thread TERMINATED
destroy semaphore
semaphore destroyed
destroy main semaphore
main semaphore destroyed
finished
numActiveThreads = 0
btShutDownExampleBrowser stopping threads
Thread with taskId 0 exiting
Thread TERMINATED
destroy semaphore
semaphore destroyed
destroy main semaphore
main semaphore destroyed