strange error isinstance() arg 2 must be a type or tuple of types

[Arthurwyf]

MKL_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=1 python run.py +experiment=speaker_ecapa_tdnn tune_model=True data/module=dogbark trainer.auto_lr_find=auto_lr_find tune_iterations=5000
/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/pytorch_lightning/core/decorators.py:65: LightningDeprecationWarning: The @auto_move_data decorator is deprecated in v1.3 and will be removed in v1.5. Please use trainer.predict instead for inference. The decorator was applied to forward
rank_zero_deprecation(
/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/pytorch_lightning/core/decorators.py:65: LightningDeprecationWarning: The @auto_move_data decorator is deprecated in v1.3 and will be removed in v1.5. Please use trainer.predict instead for inference. The decorator was applied to forward
rank_zero_deprecation(
data_folder: ${oc.env:DATA_FOLDER}
temp_folder: ${oc.env:TEMP_FOLDER}
log_folder: ${oc.env:LOG_FOLDER}
seed: 42133724
tune_model: true
tune_iterations: 5000
verify_model: false
fit_model: true
eval_model: true
load_network_from_checkpoint: null
use_cometml: ${oc.decode:${oc.env:USE_COMET_ML}}
gpus: ${oc.decode:${oc.env:NUM_GPUS}}
project_name: ecapa-tdnn
experiment_name: ${random_uuid:}
tag: ${now:%Y-%m-%d}
callbacks:
to_add:

• lr_monitor
• ram_monitor
• checkpoint
  lr_monitor:
  target: pytorch_lightning.callbacks.LearningRateMonitor
  ram_monitor:
  target: src.callbacks.memory_monitor.RamMemoryMonitor
  frequency: 100
  checkpoint:
  target: pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
  monitor: val_eer
  save_top_k: 1
  mode: min
  filename: '{epoch}.{step}.{val_eer:.4f}.best'
  save_last: true
  every_n_val_epochs: 1
  last_checkpoint_pattern: '{epoch}.{step}.{val_eer:.4f}.last'
  data:
  module:
  target: src.data.modules.speaker.voxceleb.VoxCelebDataModuleConfig
  use_voxceleb1_dev: true
  use_voxceleb1_test: true
  use_voxceleb2_dev: false
  use_voxceleb2_test: false
  all_voxceleb1_is_test_set: false
  has_train: true
  has_val: false
  has_test: true
  test_split_file_path: ${data_folder}/veri_test.txt
  shards_folder: ${data_folder}/dog_barking_shards
  extraction_folder: ${temp_folder}/dog_barking
  split_mode: equal
  train_val_ratio: 0.97
  num_val_speakers: -1
  eer_validation_pairs: 10000
  sequential_same_speaker_samples: 1
  min_unique_speakers_per_shard: 500
  discard_partial_shards: true
  voxceleb1_train_zip_path: ${data_folder}/voxceleb_archives/vox1_dev_wav.zip
  voxceleb1_test_zip_path: ${data_folder}/voxceleb_archives/vox1_test_wav.zip
  voxceleb2_train_zip_path: ${data_folder}/voxceleb_archives/vox2_dev_wav.zip
  voxceleb2_test_zip_path: ${data_folder}/voxceleb_archives/vox2_test_wav.zip
  train_collate_fn: pad_right
  val_collate_fn: default
  test_collate_fn: default
  add_batch_debug_info: false
  limit_samples: -1
  batch_processing_mode: categorical
  pipeline:
  train_pipeline:
  • selector_train
  • filterbank
  • normalizer
    val_pipeline:
  • selector_val
  • filterbank
  • normalizer
    test_pipeline:
  • filterbank
  • normalizer
    selector_train:
    target: src.data.preprocess.random_chunks.AudioChunkSelector
    selection_strategy: random
    desired_chunk_length_sec: 3
    selector_val:
    target: src.data.preprocess.random_chunks.AudioChunkSelector
    selection_strategy: start
    desired_chunk_length_sec: 3
    filterbank:
    target: src.data.preprocess.audio_features.FilterBank
    n_mels: 40
    normalizer:
    target: src.data.preprocess.input_normalisation.InputNormalizer2D
    normalize_over_channels: true
    shards:
    target: src.data.common.WebDataSetShardConfig
    samples_per_shard: 500
    use_gzip_compression: true
    shuffle_shards: true
    queue_size: 1024
    dataloader:
    target: src.data.common.SpeakerDataLoaderConfig
    train_batch_size: 32
    val_batch_size: ${data.dataloader.train_batch_size}
    test_batch_size: 1
    num_workers: 2
    pin_memory: true
    evaluator:
    target: src.evaluation.speaker.cosine_distance.CosineDistanceEvaluator
    center_before_scoring: false
    length_norm_before_scoring: false
    max_num_training_samples: 0
    network:
    target: src.lightning_modules.speaker.ecapa_tdnn.EcapaTDNNModuleConfig
    input_mel_coefficients: ${data.pipeline.filterbank.n_mels}
    lin_neurons: 192
    channels:
• 1024
• 1024
• 1024
• 1024
• 3072
  kernel_sizes:
• 5
• 3
• 3
• 3
• 1
  dilations:
• 1
• 2
• 3
• 4
• 1
  attention_channels: 128
  res2net_scale: 8
  se_channels: 128
  global_context: true
  pretrained_weights_path: null
  explicit_stat_pool_embedding_size: null
  explicit_num_speakers: null
  tokenizer:
  target: src.tokenizer.tokenizer_wav2vec2.Wav2vec2TokenizerConfig
  tokenizer_huggingface_id: facebook/wav2vec2-base-960h
  optim:
  algo:
  target: torch.optim.Adam
  lr: 0.0001
  weight_decay: 0
  betas:
  • 0.9
  • 0.999
    eps: 1.0e-08
    amsgrad: false
    schedule:
    scheduler:
    target: torch.optim.lr_scheduler.OneCycleLR
    max_lr: ${optim.algo.lr}
    total_steps: ${trainer.max_steps}
    div_factor: 25
    monitor: null
    interval: step
    frequency: null
    name: null
    loss:
    target: src.optim.loss.aam_softmax.AngularAdditiveMarginSoftMaxLoss
    input_features: 192
    output_features: 5994
    margin: 0.2
    scale: 30
    output_features: 48
    trainer:
    target: pytorch_lightning.Trainer
    gpus: ${gpus}
    accelerator: null
    num_nodes: 1
    min_epochs: null
    max_epochs: null
    min_steps: null
    max_steps: 100000
    val_check_interval: 5000
    accumulate_grad_batches: 1
    progress_bar_refresh_rate: 500
    deterministic: false
    limit_train_batches: 1.0
    limit_val_batches: 1.0
    limit_test_batches: 1.0
    fast_dev_run: false
    precision: 32
    num_sanity_val_steps: 2
    auto_lr_find: auto_lr_find
    gradient_clip_val: 0

3.9.0
pytorch_lightning.version='1.4.5'
torch.version='1.8.2+cu102'
[2023-03-27 20:54:04,608][pytorch_lightning.utilities.seed][INFO] - Global seed set to 42133724
Error executing job with overrides: ['+experiment=speaker_ecapa_tdnn', 'tune_model=True', 'data/module=dogbark', 'trainer.auto_lr_find=auto_lr_find', 'tune_iterations=5000']
Traceback (most recent call last):
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
return target(*args, **kwargs)
File "", line 33, in init
File "/home/arthur/dog_verification/w2v2-speaker-master/src/config_util.py", line 26, in post_init
post_init_type_cast(self)
File "/home/arthur/dog_verification/w2v2-speaker-master/src/config_util.py", line 41, in post_init_type_cast
elif isinstance(value, typehint_cls):
TypeError: isinstance() arg 2 must be a type or tuple of types

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/utils.py", line 211, in run_and_report
return func()
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/utils.py", line 368, in
lambda: hydra.run(
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 110, in run
_ = ret.return_value
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/core/utils.py", line 233, in return_value
raise self._return_value
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/core/utils.py", line 160, in run_job
ret.return_value = task_function(task_cfg)
File "run.py", line 38, in run
return run_train_eval_script(cfg)
File "/home/arthur/dog_verification/w2v2-speaker-master/src/main.py", line 429, in run_train_eval_script
dm = construct_data_module(cfg)
File "/home/arthur/dog_verification/w2v2-speaker-master/src/main.py", line 127, in construct_data_module
dm_cfg = hydra.utils.instantiate(cfg.data.module)
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 180, in instantiate
return instantiate_node(config, *args, recursive=recursive, convert=convert)
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 249, in instantiate_node
return _call_target(target, *args, **kwargs)
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 64, in _call_target
raise type(e)(
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
return target(*args, **kwargs)
File "", line 33, in init
File "/home/arthur/dog_verification/w2v2-speaker-master/src/config_util.py", line 26, in post_init
post_init_type_cast(self)
File "/home/arthur/dog_verification/w2v2-speaker-master/src/config_util.py", line 41, in post_init_type_cast
elif isinstance(value, typehint_cls):
TypeError: Error instantiating 'src.data.modules.speaker.voxceleb.VoxCelebDataModuleConfig' : isinstance() arg 2 must be a type or tuple of types

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "run.py", line 48, in
run()
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/main.py", line 49, in decorated_main
_run_hydra(
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/utils.py", line 367, in _run_hydra
run_and_report(
File "/home/arthur/.conda/envs/w2v2/lib/python3.8/site-packages/hydra/_internal/utils.py", line 251, in run_and_report
assert mdl is not None
AssertionError