google-research / ravens

Train robotic agents to learn pick and place with deep learning for vision-based manipulation in PyBullet. Transporter Nets, CoRL 2020.
https://transporternets.github.io
Apache License 2.0
538 stars 93 forks source link

Error: test show nothing #25

Open TriBall3 opened 2 years ago

TriBall3 commented 2 years ago

I tested the trained checkpoints file, and the following appeared in the terminal. It seems that there is no error. May I ask if this is testing? Where is the .pkl file?

(ravens) randy@randy-Precision-7920-Tower:/media/randy/299D817A2D97AD94/FTY/ravens$ python ravens/test.py --assets_root=./ravens/environments/assets/ --task=sweeping-piles --agent=transporter --n_demos=10 --n_steps=40000 --gpu=1 2022-03-31 21:14:03.592907: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 pybullet build time: Dec 1 2021 18:33:04 2022-03-31 21:14:05.890125: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1 2022-03-31 21:14:05.921645: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:73:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.75GiB deviceMemoryBandwidth: 573.69GiB/s 2022-03-31 21:14:05.922936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties: pciBusID: 0000:a6:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2022-03-31 21:14:05.922984: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 2022-03-31 21:14:05.925204: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 2022-03-31 21:14:05.927267: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10 2022-03-31 21:14:05.927634: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10 2022-03-31 21:14:05.929961: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10 2022-03-31 21:14:05.931298: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10 2022-03-31 21:14:05.935541: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 2022-03-31 21:14:05.937607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1 text argument:./ravens/environments/assets/ 2022-03-31 21:14:05.981715: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-03-31 21:14:05.999376: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2500000000 Hz 2022-03-31 21:14:06.002246: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x561a4ea86030 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2022-03-31 21:14:06.002293: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2022-03-31 21:14:06.107250: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x561a50f62630 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2022-03-31 21:14:06.107330: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2022-03-31 21:14:06.109570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:a6:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2022-03-31 21:14:06.109647: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 2022-03-31 21:14:06.109721: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 2022-03-31 21:14:06.109755: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10 2022-03-31 21:14:06.109787: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10 2022-03-31 21:14:06.109818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10 2022-03-31 21:14:06.109850: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10 2022-03-31 21:14:06.109883: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 2022-03-31 21:14:06.113529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 1 2022-03-31 21:14:06.113604: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 2022-03-31 21:14:06.621545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix: 2022-03-31 21:14:06.621590: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 1 2022-03-31 21:14:06.621613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 1: N 2022-03-31 21:14:06.623182: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10075 MB memory) -> physical GPU (device: 1, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:a6:00.0, compute capability: 7.5) Loading pre-trained model at 40000 iterations. int args: [

ZhouYFeng commented 1 year ago

Hi, @TriBall3

I am trying the Ravens. But I run it on the remote GPU server by SSH, the --disp must be set as False.

I want to know how to visualize the process of simulation like the picture below. (https://user-images.githubusercontent.com/111882855/223467716-ca7944fe-4ad7-4eac-b298-4a93182d53ac.png)

Thanks.

zwbx commented 6 months ago

same error.