TypeError: Cannot handle this data type

[usasho]

6it [00:09, 1.66s/it]
Loss: 1.4190080761909485
4it [00:06, 1.57s/it]Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/PIL/Image.py", line 2150, in fromarray
mode, rawmode = _fromarray_typemap[typekey]
KeyError: ((1, 1, 75), '|u1')

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "https://t.co/oY6LXrEgZv", line 983, in
train_seq2seq=train_seq2seq, train_postnet=train_postnet)
File "https://t.co/oY6LXrEgZv", line 717, in train
eval_model(global_step, writer, device, model, checkpoint_dir, ismultispeaker)
File "https://t.co/oY6LXrEgZv", line 414, in eval_model
writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step)
File "/usr/local/lib/python3.5/dist-packages/tensorboardX/writer.py", line 412, in add_image
self.file_writer.add_summary(image(tag, img_tensor), global_step, walltime)
File "/usr/local/lib/python3.5/dist-packages/tensorboardX/summary.py", line 205, in image
image = make_image(tensor, rescale=rescale)
File "/usr/local/lib/python3.5/dist-packages/tensorboardX/summary.py", line 243, in make_image
image = Image.fromarray(tensor)
File "/usr/lib/python3/dist-packages/PIL/Image.py", line 2153, in fromarray
raise TypeError("Cannot handle this data type")
TypeError: Cannot handle this data type

[usasho]

上げました
Pillow系のerrorだとは思うのですが、
解決策が無いため、質問させて頂きました

[usasho]

Torchのバージョンもあるかもしれません、
古いlinuxなら動きましたので、
Dtypeの記述が逆なのかもしれませんね
KeyError: ((1, 1, 75), '|u1')
Torch 0.4.1から色々変わってるみたいです

[usasho]

Although this time PIL error, because the cause was found out, I will report it here. Please do not make tensorboardX version 1.2 or later.

[creatorrr]

Nice catch, @usasho .

Similar error reported for tensorboardX==1.4 in other packages, for example:
inferno-pytorch/inferno#142 (comment)

[Martin-Laclaustra]

Solved in tensorboardX v1.6.
It is not necessary to constrain the version any more.

[tripzero]

I'm seeing this in tensorboardX 1.7:

python3 train.py --data-root=./out/ --preset=presets/deepvoice3_vctk.json 
Command line args:
 {'--checkpoint': None,
 '--checkpoint-dir': 'checkpoints',
 '--checkpoint-postnet': None,
 '--checkpoint-seq2seq': None,
 '--data-root': './out/',
 '--help': False,
 '--hparams': '',
 '--load-embedding': None,
 '--log-event-path': None,
 '--preset': 'presets/deepvoice3_vctk.json',
 '--reset-optimizer': False,
 '--restore-parts': None,
 '--speaker-id': None,
 '--train-postnet-only': False,
 '--train-seq2seq-only': False}
Training whole model
Training seq2seq model
Hyperparameters:
  adam_beta1: 0.5
  adam_beta2: 0.9
  adam_eps: 1e-06
  allow_clipping_in_normalization: True
  amsgrad: False
  batch_size: 16
  binary_divergence_weight: 0.1
  builder: deepvoice3_multispeaker
  checkpoint_interval: 10000
  clip_thresh: 0.1
  converter_channels: 256
  decoder_channels: 256
  downsample_step: 4
  dropout: 0.050000000000000044
  embedding_weight_std: 0.1
  encoder_channels: 512
  eval_interval: 10000
  fft_size: 1024
  fmax: 7600
  fmin: 125
  force_monotonic_attention: True
  freeze_embedding: False
  frontend: en
  guided_attention_sigma: 0.4
  hop_size: 256
  ignore_recognition_level: 2
  initial_learning_rate: 0.0005
  kernel_size: 3
  key_position_rate: 7.6
  key_projection: True
  lr_schedule: noam_learning_rate_decay
  lr_schedule_kwargs: {}
  masked_loss_weight: 0.5
  max_positions: 1024
  min_level_db: -100
  min_text: 20
  n_speakers: 108
  name: deepvoice3
  nepochs: 2000
  num_mels: 80
  num_workers: 2
  outputs_per_step: 1
  padding_idx: 0
  pin_memory: True
  power: 1.4
  preemphasis: 0.97
  priority_freq: 3000
  priority_freq_weight: 0.0
  process_only_htk_aligned: False
  query_position_rate: 2.0
  ref_level_db: 20
  replace_pronunciation_prob: 0.5
  rescaling: False
  rescaling_max: 0.999
  sample_rate: 22050
  save_optimizer_state: True
  speaker_embed_dim: 16
  speaker_embedding_weight_std: 0.05
  text_embed_dim: 256
  trainable_positional_encodings: False
  use_decoder_state_for_postnet_input: True
  use_guided_attention: True
  use_memory_mask: True
  value_projection: True
  weight_decay: 0.0
  window_ahead: 3
  window_backward: 1
Log event path: log/run-test2019-06-24_17:16:26.783904
2755it [57:20,  2.48s/it]
Loss: 0.5053848431136344
2755it [57:40,  2.60s/it]
Loss: 0.4032350966264029
2755it [57:53,  2.69s/it]
Loss: 0.37271299628294097
1735it [36:28,  1.21s/it]Save intermediate states at step 10000
Traceback (most recent call last):
  File "/usr/lib/python3/dist-packages/PIL/Image.py", line 2428, in fromarray
    mode, rawmode = _fromarray_typemap[typekey]
KeyError: ((1, 1, 35), '|u1')

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "train.py", line 981, in <module>
    train_seq2seq=train_seq2seq, train_postnet=train_postnet)
  File "train.py", line 709, in train
    mel, y, input_lengths, checkpoint_dir)
  File "train.py", line 445, in save_states
    writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/writer.py", line 533, in add_image
    image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/summary.py", line 224, in image
    image = make_image(tensor, rescale=rescale)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/summary.py", line 262, in make_image
    image = Image.fromarray(tensor)
  File "/usr/lib/python3/dist-packages/PIL/Image.py", line 2431, in fromarray
    raise TypeError("Cannot handle this data type")
TypeError: Cannot handle this data type```

[tripzero]

Anyone else seeing this? Should this be reopened?

[01ghost13]

Was caught in google colab environment.
tensorboardX 1.8:

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/PIL/Image.py", line 2416, in fromarray
    mode, rawmode = _fromarray_typemap[typekey]
KeyError: ((1, 1, 75), '|u1')

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "train.py", line 981, in <module>
    train_seq2seq=train_seq2seq, train_postnet=train_postnet)
  File "train.py", line 715, in train
    eval_model(global_step, writer, device, model, checkpoint_dir, ismultispeaker)
  File "train.py", line 412, in eval_model
    writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/writer.py", line 548, in add_image
    image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/summary.py", line 216, in image
    image = make_image(tensor, rescale=rescale)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/summary.py", line 256, in make_image
    image = Image.fromarray(tensor)
  File "/usr/local/lib/python3.6/dist-packages/PIL/Image.py", line 2419, in fromarray
    raise TypeError("Cannot handle this data type")
TypeError: Cannot handle this data type
Exception ignored in: <bound method tqdm.__del__ of 46it [00:15,  3.69it/s]>
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py", line 931, in __del__
    self.close()
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py", line 1133, in close
    self._decr_instances(self)
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_tqdm.py", line 496, in _decr_instances
    cls.monitor.exit()
  File "/usr/local/lib/python3.6/dist-packages/tqdm/_monitor.py", line 52, in exit
    self.join()
  File "/usr/lib/python3.6/threading.py", line 1053, in join
    raise RuntimeError("cannot join current thread")
RuntimeError: cannot join current thread

[harirawat]

Getting same error in google colab.
tensorboardX 1.8

Command line args:
 {'--checkpoint': None,
 '--checkpoint-dir': 'checkpoints',
 '--checkpoint-postnet': None,
 '--checkpoint-seq2seq': None,
 '--data-root': './datasets/processeddata',
 '--help': False,
 '--hparams': 'builder=deepvoice3,num_workers=1,n_speakers=1,batch_size=6,checkpoint_interval=100',
 '--load-embedding': None,
 '--log-event-path': None,
 '--preset': 'presets/deepvoice3_vctk.json',
 '--reset-optimizer': False,
 '--restore-parts': '20171222_deepvoice3_vctk108_checkpoint_step000300000.pth',
 '--speaker-id': None,
 '--train-postnet-only': False,
 '--train-seq2seq-only': False}
Training whole model
Training seq2seq model
Hyperparameters:
  adam_beta1: 0.5
  adam_beta2: 0.9
  adam_eps: 1e-06
  allow_clipping_in_normalization: True
  amsgrad: False
  batch_size: 6
  binary_divergence_weight: 0.1
  builder: deepvoice3
  checkpoint_interval: 100
  clip_thresh: 0.1
  converter_channels: 256
  decoder_channels: 256
  downsample_step: 4
  dropout: 0.050000000000000044
  embedding_weight_std: 0.1
  encoder_channels: 512
  eval_interval: 10000
  fft_size: 1024
  fmax: 7600
  fmin: 125
  force_monotonic_attention: True
  freeze_embedding: False
  frontend: en
  guided_attention_sigma: 0.4
  hop_size: 256
  ignore_recognition_level: 2
  initial_learning_rate: 0.0005
  kernel_size: 3
  key_position_rate: 7.6
  key_projection: True
  lr_schedule: noam_learning_rate_decay
  lr_schedule_kwargs: {}
  masked_loss_weight: 0.5
  max_positions: 1024
  min_level_db: -100
  min_text: 20
  n_speakers: 1
  name: deepvoice3
  nepochs: 2000
  num_mels: 80
  num_workers: 1
  outputs_per_step: 1
  padding_idx: 0
  pin_memory: True
  power: 1.4
  preemphasis: 0.97
  preset: deepvoice3_vctk.json
  priority_freq: 3000
  priority_freq_weight: 0.0
  process_only_htk_aligned: False
  query_position_rate: 2.0
  ref_level_db: 20
  replace_pronunciation_prob: 0.5
  rescaling: False
  rescaling_max: 0.999
  sample_rate: 22050
  save_optimizer_state: True
  speaker_embed_dim: 16
  speaker_embedding_weight_std: 0.05
  text_embed_dim: 256
  trainable_positional_encodings: False
  use_decoder_state_for_postnet_input: True
  use_guided_attention: True
  use_memory_mask: True
  value_projection: True
  weight_decay: 0.0
  window_ahead: 3
  window_backward: 1
Restore part of the model from: 20171222_deepvoice3_vctk108_checkpoint_step000300000.pth
Log event path: log/run-test2019-07-22_11:16:19.248580
0it [00:00, ?it/s]THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=663 error=11 : invalid argument
25it [00:02,  9.96it/s]
Loss: 18.01471851348877
25it [00:02,  9.80it/s]
Loss: 7.743541326522827
25it [00:02, 10.32it/s]
Loss: 3.732187213897705
25it [00:02, 10.08it/s]
Loss: 2.7462184238433838
0it [00:00, ?it/s]Save intermediate states at step 100

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/PIL/Image.py", line 2416, in fromarray
    mode, rawmode = _fromarray_typemap[typekey]
KeyError: ((1, 1, 145), '|u1')

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "train.py", line 981, in <module>
    train_seq2seq=train_seq2seq, train_postnet=train_postnet)
  File "train.py", line 709, in train
    mel, y, input_lengths, checkpoint_dir)
  File "train.py", line 445, in save_states
    writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/writer.py", line 548, in add_image
    image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/summary.py", line 216, in image
    image = make_image(tensor, rescale=rescale)
  File "/usr/local/lib/python3.6/dist-packages/tensorboardX/summary.py", line 256, in make_image
    image = Image.fromarray(tensor)
  File "/usr/local/lib/python3.6/dist-packages/PIL/Image.py", line 2419, in fromarray
    raise TypeError("Cannot handle this data type")
TypeError: Cannot handle this data type

[abenbihi]

I was having the same issue when feeding the image in HxWxC format. I switched to CxHxW and it solved it.