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so-vits-svc
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Numpy deprecated error in local copy? #15

Closed mya2152 closed 1 year ago

mya2152 commented 1 year ago

Win10 x64 48GB RAM VSCode Python 3.9.2 NVIDIA M1000M CUDA toolkit installed latest version (initial GPU step didn't show any errors)

All steps completed fine but right at final file conversion step when pressing the convert button, this error appears:

### Error: Output exceeds the size limit. Open the full output data in a text editor--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[1], line 80, in InferenceGui.init..convert_cb(btn) 79 def convert_cb(btn): ---> 80 self.convert()

Cell In[1], line 112, in InferenceGui.convert(self) 109 print(spkpth2) 110 print(os.path.exists(spkpth2)) --> 112 svc_model = Svc(speaker["model_path"], speaker["cfg_path"], 113 cluster_model_path=speaker["cluster_path"]) 115 input_filepaths = [f for f in glob.glob('/content/*/.*', recursive=True) 116 if f not in existing_files and 117 any(f.endswith(ex) for ex in ['.wav','.flac','.mp3','.ogg','.opus'])] 118 for name in input_filepaths:

File E:\aivocals\bin\so-vits-svc-eff-4.0\inference\infer_tool.py:128, in Svc.init(self, net_g_path, config_path, device, cluster_model_path) 125 self.f0_cache = {} 127 # 加载hubert --> 128 self.hubert_model = sovits_utils.get_hubert_model().to(self.dev) 129 self.load_model() 130 if os.path.exists(cluster_model_path):

File E:\aivocals\bin\so-vits-svc-eff-4.0\sovits_utils.py:236, in get_hubert_model() 234 vec_path = "hubert/checkpoint_best_legacy_500.pt" ...

AttributeError: module 'numpy' has no attribute 'object'. np.object was a deprecated alias for the builtin object. To avoid this error in existing code, use object by itself. Doing this will not modify any behavior and is safe. 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

### // End of output

Any ideas ?

effusiveperiscope commented 1 year ago

Do you have a full traceback that shows which library/code the numpy error came from?

mya2152 commented 1 year ago

Do you have a full traceback that shows which library/code the numpy error came from?

Please see below:

{ "name": "AttributeError", "message": "module 'numpy' has no attribute 'object'.\nnp.object was a deprecated alias for the builtin object. To avoid this error in existing code, use object by itself. Doing this will not modify any behavior and is safe. \nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations", "stack": "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)\nCell \u001b[1;32mIn[1], line 80\u001b[0m, in \u001b[0;36mInferenceGui.init..convert_cb\u001b[1;34m(btn)\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconvert_cb\u001b[39m(btn):\n\u001b[1;32m---> 80\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconvert()\n\nCell \u001b[1;32mIn[1], line 112\u001b[0m, in \u001b[0;36mInferenceGui.convert\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[39mprint\u001b[39m(spkpth2)\n\u001b[0;32m 110\u001b[0m \u001b[39mprint\u001b[39m(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexists(spkpth2))\n\u001b[1;32m--> 112\u001b[0m svc_model \u001b[39m=\u001b[39m Svc(speaker[\u001b[39m\"\u001b[39;49m\u001b[39mmodel_path\u001b[39;49m\u001b[39m\"\u001b[39;49m], speaker[\u001b[39m\"\u001b[39;49m\u001b[39mcfg_path\u001b[39;49m\u001b[39m\"\u001b[39;49m], \n\u001b[0;32m 113\u001b[0m cluster_model_path\u001b[39m=\u001b[39;49mspeaker[\u001b[39m\"\u001b[39;49m\u001b[39mcluster_path\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n\u001b[0;32m 115\u001b[0m input_filepaths \u001b[39m=\u001b[39m [f \u001b[39mfor\u001b[39;00m f \u001b[39min\u001b[39;00m glob\u001b[39m.\u001b[39mglob(\u001b[39m'\u001b[39m\u001b[39m/content/*/.*\u001b[39m\u001b[39m'\u001b[39m, recursive\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m 116\u001b[0m \u001b[39mif\u001b[39;00m f \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m existing_files \u001b[39mand\u001b[39;00m \n\u001b[0;32m 117\u001b[0m \u001b[39many\u001b[39m(f\u001b[39m.\u001b[39mendswith(ex) \u001b[39mfor\u001b[39;00m ex \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39m.wav\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m.flac\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m.mp3\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m.ogg\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39m.opus\u001b[39m\u001b[39m'\u001b[39m])]\n\u001b[0;32m 118\u001b[0m \u001b[39mfor\u001b[39;00m name \u001b[39min\u001b[39;00m input_filepaths:\n\nFile \u001b[1;32mE:\aivocals\bin\so-vits-svc-eff-4.0\inference\infer_tool.py:128\u001b[0m, in \u001b[0;36mSvc.init\u001b[1;34m(self, net_g_path, config_path, device, cluster_model_path)\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mf0_cache \u001b[39m=\u001b[39m {}\n\u001b[0;32m 127\u001b[0m \u001b[39m# 加载hubert\u001b[39;00m\n\u001b[1;32m--> 128\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhubert_model \u001b[39m=\u001b[39m sovits_utils\u001b[39m.\u001b[39;49mget_hubert_model()\u001b[39m.\u001b[39mto(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdev)\n\u001b[0;32m 129\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mload_model()\n\u001b[0;32m 130\u001b[0m \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001bvec_path:\n\nFile \u001b[1;32mE:\aivocals\bin\so-vits-svc-eff-4.0\sovits_utils.py:236\u001b[0m, in \u001b[0;36mget_hubert_model\u001b[1;34m()\u001b[0m\n\u001b[0;32m 234\u001b[0m vec_path \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mhubert/checkpoint_best_legacy_500.pt\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 235\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mload model(s) from \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001bvec_path)\n\u001b[1;32m--> 236\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfairseq\u001b[39;00m \u001b[39mimport\u001b[39;00m checkpoint_utils\n\u001b[0;32m 237\u001b[0m models, saved_cfg, task \u001b[39m=\u001b[39m checkpoint_utils\u001b[39m.\u001b[39mload_model_ensemble_and_task(\n\u001b[0;32m 238\u001b[0m [vec_path],\n\u001b[0;32m 239\u001b[0m suffix\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 240\u001b[0m )\n\u001b[0;32m 241\u001b[0m model \u001b[39m=\u001b[39m models[\u001b[39m0\u001b[39m]\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\fairseq\init.py:21\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[39m# backwards compatibility to support from fairseq.X import Y\u001b[39;00m\n\u001b[0;32m 20\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfairseq\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdistributed\u001b[39;00m \u001b[39mimport\u001b[39;00m utils \u001b[39mas\u001b[39;00m distributed_utils\n\u001b[1;32m---> 21\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfairseq\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mlogging\u001b[39;00m \u001b[39mimport\u001b[39;00m meters, metrics, progress_bar \u001b[39m# noqa\u001b[39;00m\n\u001b[0;32m 23\u001b[0m sys\u001b[39m.\u001b[39mmodules[\u001b[39m\"\u001b[39m\u001b[39mfairseq.distributed_utils\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m distributed_utils\n\u001b[0;32m 24\u001b[0m sys\u001b[39m.\u001b[39mmodules[\u001b[39m\"\u001b[39m\u001b[39mfairseq.meters\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m meters\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\fairseq\logging\progress_bar.py:407\u001b[0m\n\u001b[0;32m 405\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 406\u001b[0m _tensorboard_writers \u001b[39m=\u001b[39m {}\n\u001b[1;32m--> 407\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtorch\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtensorboard\u001b[39;00m \u001b[39mimport\u001b[39;00m SummaryWriter\n\u001b[0;32m 408\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n\u001b[0;32m 409\u001b[0m \u001b[39mtry\u001b[39;00m:\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\torch\utils\tensorboard\init.py:12\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[39mdel\u001b[39;00m LooseVersion\n\u001b[0;32m 10\u001b[0m \u001b[39mdel\u001b[39;00m tensorboard\n\u001b[1;32m---> 12\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mwriter\u001b[39;00m \u001b[39mimport\u001b[39;00m FileWriter, SummaryWriter \u001b[39m# noqa: F401\u001b[39;00m\n\u001b[0;32m 13\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msummary\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mwriter\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mrecord_writer\u001b[39;00m \u001b[39mimport\u001b[39;00m RecordWriter \u001b[39m# noqa: F401\u001b[39;00m\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\torch\utils\tensorboard\writer.py:13\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcompat\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mproto\u001b[39;00m \u001b[39mimport\u001b[39;00m event_pb2\n\u001b[0;32m 12\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mprojector\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mprojector_config_pb2\u001b[39;00m \u001b[39mimport\u001b[39;00m ProjectorConfig\n\u001b[1;32m---> 13\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msummary\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mwriter\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mevent_file_writer\u001b[39;00m \u001b[39mimport\u001b[39;00m EventFileWriter\n\u001b[0;32m 15\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_convert_np\u001b[39;00m \u001b[39mimport\u001b[39;00m make_np\n\u001b[0;32m 16\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39m_embedding\u001b[39;00m \u001b[39mimport\u001b[39;00m (\n\u001b[0;32m 17\u001b[0m make_mat,\n\u001b[0;32m 18\u001b[0m make_sprite,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 21\u001b[0m get_embedding_info,\n\u001b[0;32m 22\u001b[0m )\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\tensorboard\summary\init.py:25\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[39m# If the V1 summary API is accessible, load and re-export it here.\u001b[39;00m\n\u001b[0;32m 24\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 25\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msummary\u001b[39;00m \u001b[39mimport\u001b[39;00m v1 \u001b[39m# noqa: F401\u001b[39;00m\n\u001b[0;32m 26\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n\u001b[0;32m 27\u001b[0m \u001b[39mpass\u001b[39;00m\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\tensorboard\summary\v1.py:26\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39maudio\u001b[39;00m \u001b[39mimport\u001b[39;00m summary \u001b[39mas\u001b[39;00m _audio_summary\n\u001b[0;32m 25\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcustom_scalar\u001b[39;00m \u001b[39mimport\u001b[39;00m summary \u001b[39mas\u001b[39;00m _custom_scalar_summary\n\u001b[1;32m---> 26\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mhistogram\u001b[39;00m \u001b[39mimport\u001b[39;00m summary \u001b[39mas\u001b[39;00m _histogram_summary\n\u001b[0;32m 27\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mimage\u001b[39;00m \u001b[39mimport\u001b[39;00m summary \u001b[39mas\u001b[39;00m _image_summary\n\u001b[0;32m 28\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpr_curve\u001b[39;00m \u001b[39mimport\u001b[39;00m summary \u001b[39mas\u001b[39;00m _pr_curve_summary\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\tensorboard\plugins\histogram\summary.py:38\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m 37\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mhistogram\u001b[39;00m \u001b[39mimport\u001b[39;00m metadata\n\u001b[1;32m---> 38\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mhistogram\u001b[39;00m \u001b[39mimport\u001b[39;00m summary_v2\n\u001b[0;32m 41\u001b[0m \u001b[39m# Export V2 versions.\u001b[39;00m\n\u001b[0;32m 42\u001b[0m histogram \u001b[39m=\u001b[39m summary_v2\u001b[39m.\u001b[39mhistogram\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\tensorboard\plugins\histogram\summary_v2.py:38\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplugins\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mhistogram\u001b[39;00m \u001b[39mimport\u001b[39;00m metadata\n\u001b[0;32m 37\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutil\u001b[39;00m \u001b[39mimport\u001b[39;00m lazy_tensor_creator\n\u001b[1;32m---> 38\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutil\u001b[39;00m \u001b[39mimport\u001b[39;00m tensor_util\n\u001b[0;32m 41\u001b[0m DEFAULT_BUCKET_COUNT \u001b[39m=\u001b[39m \u001b[39m30\u001b[39m\n\u001b[0;32m 44\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mhistogram\u001b[39m(name, data, step\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, buckets\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, description\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\tensorboard\util\tensor_util.py:24\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msix\u001b[39;00m\n\u001b[0;32m 23\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcompat\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mproto\u001b[39;00m \u001b[39mimport\u001b[39;00m tensor_pb2\n\u001b[1;32m---> 24\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcompat\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtensorflow_stub\u001b[39;00m \u001b[39mimport\u001b[39;00m dtypes, compat, tensor_shape\n\u001b[0;32m 27\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mExtractBitsFromFloat16\u001b[39m(x):\n\u001b[0;32m 28\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39masarray(x, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mfloat16)\u001b[39m.\u001b[39mview(np\u001b[39m.\u001b[39muint16)\u001b[39m.\u001b[39mitem()\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\tensorboard\compat\tensorflow_stub\init.py:25\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcompat\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mproto\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmeta_graph_pb2\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m\u001b[39m \u001b[39m# noqa\u001b[39;00m\n\u001b[0;32m 24\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtensorboard\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcompat\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mproto\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msummary_pb2\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m\u001b[39m \u001b[39m# noqa\u001b[39;00m\n\u001b[1;32m---> 25\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mdtypes\u001b[39;00m \u001b[39mimport\u001b[39;00m as_dtype \u001b[39m# noqa\u001b[39;00m\n\u001b[0;32m 26\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mdtypes\u001b[39;00m \u001b[39mimport\u001b[39;00m DType \u001b[39m# noqa\u001b[39;00m\n\u001b[0;32m 27\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mdtypes\u001b[39;00m \u001b[39mimport\u001b[39;00m string \u001b[39m# noqa\u001b[39;00m\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\tensorboard\compat\tensorflow_stub\dtypes.py:572\u001b[0m\n\u001b[0;32m 554\u001b[0m np_resource \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mdtype([(\u001b[39m\"\u001b[39m\u001b[39mresource\u001b[39m\u001b[39m\"\u001b[39m, np\u001b[39m.\u001b[39mubyte)])\n\u001b[0;32m 556\u001b[0m \u001b[39m# Standard mappings between types_pb2.DataType values and numpy.dtypes.\u001b[39;00m\n\u001b[0;32m 557\u001b[0m _NP_TO_TF \u001b[39m=\u001b[39m \u001b[39mfrozenset\u001b[39m(\n\u001b[0;32m 558\u001b[0m [\n\u001b[0;32m 559\u001b[0m (np\u001b[39m.\u001b[39mfloat16, float16),\n\u001b[0;32m 560\u001b[0m (np\u001b[39m.\u001b[39mfloat32, float32),\n\u001b[0;32m 561\u001b[0m (np\u001b[39m.\u001b[39mfloat64, float64),\n\u001b[0;32m 562\u001b[0m (np\u001b[39m.\u001b[39mint32, int32),\n\u001b[0;32m 563\u001b[0m (np\u001b[39m.\u001b[39mint64, int64),\n\u001b[0;32m 564\u001b[0m (np\u001b[39m.\u001b[39muint8, uint8),\n\u001b[0;32m 565\u001b[0m (np\u001b[39m.\u001b[39muint16, uint16),\n\u001b[0;32m 566\u001b[0m (np\u001b[39m.\u001b[39muint32, uint32),\n\u001b[0;32m 567\u001b[0m (np\u001b[39m.\u001b[39muint64, uint64),\n\u001b[0;32m 568\u001b[0m (np\u001b[39m.\u001b[39mint16, int16),\n\u001b[0;32m 569\u001b[0m (np\u001b[39m.\u001b[39mint8, int8),\n\u001b[0;32m 570\u001b[0m (np\u001b[39m.\u001b[39mcomplex64, complex64),\n\u001b[0;32m 571\u001b[0m (np\u001b[39m.\u001b[39mcomplex128, complex128),\n\u001b[1;32m--> 572\u001b[0m (np\u001b[39m.\u001b[39;49mobject, string),\n\u001b[0;32m 573\u001b[0m (np\u001b[39m.\u001b[39mbool, \u001b[39mbool\u001b[39m),\n\u001b[0;32m 574\u001b[0m (_np_qint8, qint8),\n\u001b[0;32m 575\u001b[0m (_np_quint8, quint8),\n\u001b[0;32m 576\u001b[0m (_np_qint16, qint16),\n\u001b[0;32m 577\u001b[0m (_np_quint16, quint16),\n\u001b[0;32m 578\u001b[0m (_np_qint32, qint32),\n\u001b[0;32m 579\u001b[0m \u001b[39m# TODO(#1677): _np_bfloat16 is defined as 0. This causes as_dtype to\u001b[39;00m\n\u001b[0;32m 580\u001b[0m \u001b[39m# error. Add below back after we fix TF_bfloat16_type.\u001b[39;00m\n\u001b[0;32m 581\u001b[0m \u001b[39m# (_np_bfloat16, bfloat16),\u001b[39;00m\n\u001b[0;32m 582\u001b[0m ]\n\u001b[0;32m 583\u001b[0m )\n\u001b[0;32m 584\u001b[0m _TF_TO_NP \u001b[39m=\u001b[39m {\n\u001b[0;32m 585\u001b[0m types_pb2\u001b[39m.\u001b[39mDT_HALF: np\u001b[39m.\u001b[39mfloat16,\n\u001b[0;32m 586\u001b[0m types_pb2\u001b[39m.\u001b[39mDT_FLOAT: np\u001bqint8, quint8, qint16, quint16, qint32\u001b[0m\n\u001b[0;32m 629\u001b[0m types_pb2\u001b[39m.\u001b[39mDT_BFLOAT16_REF: _np_bfloat16,\n\u001b[0;32m 630\u001b[0m }\n\u001b[0;32m 632\u001b[0m _QUANTIZED_DTYPES_NO_REF \u001b[39m=\u001b[39m \u001b[39mfrozenset\u001b[39m([qint8, quint8, qint16, quint16, qint32])\n\nFile \u001b[1;32mc:\Python\Python392\lib\site-packages\numpy\init.py:305\u001b[0m, in \u001b[0;36mgetattr\u001battr\u001b[0m\n\u001b[0;32m 300\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m 301\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIn the future np.\u001b[39m\u001b[39m{\u001b[39;00mattr\u001b[39m}\u001b[39;00m\u001b[39m will be defined as the \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 302\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcorresponding NumPy scalar.\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFutureWarning\u001b[39;00m, stacklevel\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n\u001b[0;32m 304\u001b[0m \u001b[39mif\u001b[39;00m attr \u001b[39min\u001b[39;00m former_attrs:\n\u001b[1;32m--> 305\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(former_attrs[attr])\n\u001b[0;32m 307\u001b[0m \u001b[39m# Importing Tester requires importing all of UnitTest which is not a\u001b[39;00m\n\u001b[0;32m 308\u001b[0m \u001b[39m# cheap import Since it is mainly used in test suits, we lazy import it\u001b[39;00m\n\u001b[0;32m 309\u001b[0m \u001b[39m# here to save on the order of 10 ms of import time for most users\u001b[39;00m\n\u001b[0;32m 310\u001b[0m \u001b[39m#\u001b[39;00m\n\u001b[0;32m 311\u001b[0m \u001b[39m# The previous way Tester was imported also had a side effect of adding\u001b[39;00m\n\u001b[0;32m 312\u001b[0m \u001b[39m# the full numpy.testing namespace\u001b[39;00m\n\u001b[0;32m 313\u001b[0m \u001b[39mif\u001b[39;00m attr \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m'\u001b[39m:\n\n\u001b[1;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'object'.\nnp.object was a deprecated alias for the builtin object. To avoid this error in existing code, use object by itself. Doing this will not modify any behavior and is safe. \nThe aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations" }

effusiveperiscope commented 1 year ago

What version of numpy are you using? The version listed in requirements.txt is 1.23.5.

mya2152 commented 1 year ago

What version of numpy are you using? The version listed in requirements.txt is 1.23.5.

My installation is currently on v1.24.2, it should still run correct?

effusiveperiscope commented 1 year ago

The np.object alias seems to have been deprecated in 1.24.0. I'm not sure tensorboard has been updated to reflect this. You may want to downgrade.

mya2152 commented 1 year ago

The np.object alias seems to have been deprecated in 1.24.0. I'm not sure tensorboard has been updated to reflect this. You may want to downgrade.

Solved, thanks again!