Conv2DCustomBackpropFilterOp only supports NHWC error

Hi there! I'm trying to run this project (BTW, I'm looking forward to it!) and when it gets time to start creating a wavenet training model via "python train.py", I experience this error. Any workarounds? I'm using tensorflow-gpu on a Windows machine. Uninstalling tensorflow-gpu and installing tensorflow does not help.
Generated 328 test batches of size 1 in 0.297 sec
2018-06-18 13:30:33.533678: E T:\src\github\tensorflow\tensorflow\core\common_runtime\executor.cc:660] Executor failed to create kernel. Invalid argument: Conv2DCustomBackpropFilterOp only supports NHWC.
[[Node: model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Conv2DBackpropFilter = Conv2DBackpropFilter[T=DT_FLOAT, _class=["loc:@model/optimizer/clip_by_global_norm/mul_197"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 16], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/optimizer/gradients/model/inference/conv2d_transpose/BiasAdd_grad/tuple/control_dependency/_2947, model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Shape, model/inference/ExpandDims_1/_2949)]]
Exiting due to Exception: Conv2DCustomBackpropFilterOp only supports NHWC.
[[Node: model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Conv2DBackpropFilter = Conv2DBackpropFilter[T=DT_FLOAT, _class=["loc:@model/optimizer/clip_by_global_norm/mul_197"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 16], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/optimizer/gradients/model/inference/conv2d_transpose/BiasAdd_grad/tuple/control_dependency/_2947, model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Shape, model/inference/ExpandDims_1/_2949)]]
Caused by op 'model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Conv2DBackpropFilter', defined at:
File "train.py", line 128, in
main()
File "train.py", line 122, in main
train(args, log_dir, hparams)
File "train.py", line 76, in train
checkpoint = wavenet_train(args, log_dir, hparams, input_path)
File "D:\DL2\Tacotron-2\wavenet_vocoder\train.py", line 244, in wavenet_train
return train(log_dir, args, hparams, input_path)
File "D:\DL2\Tacotron-2\wavenet_vocoder\train.py", line 167, in train
model, stats = model_train_mode(args, feeder, hparams, global_step)
File "D:\DL2\Tacotron-2\wavenet_vocoder\train.py", line 119, in model_train_mode
model.add_optimizer(global_step)
File "D:\DL2\Tacotron-2\wavenet_vocoder\models\wavenet.py", line 365, in add_optimizer
gradients, variables = zip(*optimizer.compute_gradients(self.loss))
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\training\optimizer.py", line 526, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 494, in gradients
gate_gradients, aggregation_method, stop_gradients)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 636, in _GradientsHelper
lambda: grad_fn(op, *out_grads))
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 385, in _MaybeCompile
return grad_fn()  # Exit early
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 636, in
lambda: grad_fn(op, *out_grads))
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\nn_grad.py", line 54, in _Conv2DBackpropInputGrad
data_format=op.get_attr("data_format")),
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1190, in conv2d_backprop_filter
dilations=dilations, name=name)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
...which was originally created as op 'model/inference/conv2d_transpose/conv2d_transpose', defined at:
File "train.py", line 128, in
main()
[elided 3 identical lines from previous traceback]
File "D:\DL2\Tacotron-2\wavenet_vocoder\train.py", line 167, in train
model, stats = model_train_mode(args, feeder, hparams, global_step)
File "D:\DL2\Tacotron-2\wavenet_vocoder\train.py", line 117, in model_train_mode
feeder.input_lengths, x=feeder.inputs)
File "D:\DL2\Tacotron-2\wavenet_vocoder\models\wavenet.py", line 169, in initialize
y_hat = self.step(x, c, g, softmax=False) #softmax is automatically computed inside softmax_cross_entropy if needed
File "D:\DL2\Tacotron-2\wavenet_vocoder\models\wavenet.py", line 435, in step
c = transposed_conv(c)
File "D:\DL2\Tacotron-2\wavenet_vocoder\models\modules.py", line 333, in call
return self.convt(inputs)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\layers\base.py", line 717, in call
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\layers\convolutional.py", line 1667, in call
data_format=utils.convert_data_format(self.data_format, ndim=4))
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1254, in conv2d_transpose
name=name)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1340, in conv2d_backprop_input
dilations=dilations, name=name)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\Users\merko\AppData\Local\conda\conda\envs\DL2\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Conv2DCustomBackpropFilterOp only supports NHWC.
[[Node: model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Conv2DBackpropFilter = Conv2DBackpropFilter[T=DT_FLOAT, _class=["loc:@model/optimizer/clip_by_global_norm/mul_197"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 16], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/optimizer/gradients/model/inference/conv2d_transpose/BiasAdd_grad/tuple/control_dependency/_2947, model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Shape, model/inference/ExpandDims_1/_2949)]]
2018-06-18 13:30:33.575389: W T:\src\github\tensorflow\tensorflow\core\kernels\queue_base.cc:277] _1_datafeeder/eval_queue: Skipping cancelled enqueue attempt with queue not closed
2018-06-18 13:30:33.581458: W T:\src\github\tensorflow\tensorflow\core\kernels\queue_base.cc:277] _0_datafeeder/intput_queue: Skipping cancelled enqueue attempt with queue not closed
Traceback (most recent call last):
File "train.py", line 128, in
main()
File "train.py", line 122, in main
train(args, log_dir, hparams)
File "train.py", line 78, in train
raise ('Error occured while training Wavenet, Exiting!')
TypeError: exceptions must derive from BaseException

Hey, I got the same issue.
Did you find the solution?

I am also having this issue.
@ChenXi1992 Are you also using Windows? Also, (@merkoski too), what versions of python, tensorflow-gpu, etc, are you using, and did you tweak anything? Maybe we can find a common culprit.

@DanRuta  Hi, I solved it. It turns out the tf can't detect the GPU, even though you got no error after installed the package.
Just run this, and see whether is correct:
tf.__version__sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
Also check the NVIDIA information:
nvidia-smi
It should be able to solve it.

I'm afraid this doesn't help. In my case the GPU does work just fine, throughout the Tacotron modeling; it fails with the "Conv2DCustomBackpropFilterOp only supports NHWC" error during the Wavenet modeling.
I am on Windows10, Python 3.6.5, Tensorflow (GPU) 1.8.0.

GPU seems fine for me, also. Tacotron seems to be using it fine, but it fails at Wavenet. I haven't tried any other Wavenet implementations, yet, as I'm away. I had to set outputs_per_step to 5, in hparams.py (#4), but changed nothing in the code from the latest version. I'll dig around some more when I get some time.
I'm running Python 3.6.4, with Tensorflow gpu 1.8.0.

Hey @DanRuta I had to do something similar, in my case specifying tacotron_cache_size of 3 in hparams (via command line in my case). I had to do this in order for the data to fit onto the GPU, but I wonder if a non-standard hparams is causing the issue somehow....

One-dimensional  data: ”NWC”
two-dimensional  data:  "NHWC"
whether can convert ”NWC” to "NHWC" with "conv2d"?

please install new version tf, new version have fixed it.

Has anyone been able to find a working solution to this problem? For a while I've been stuck with the same error now :/

Not me, unfortunately. The existing suggestions were not applicable or didn't solve it.

Does anyone have any new leads on getting Wavenet to work? I looked all over github and I can't seem to find a solution. I tried isolating my GPU for training, changing h-parameters, and changing data_formats to 'NCHW.' However, nothing seems to quite work :(.
I'm running Python 3.6.2 and Tensorflow GPU 1.8.0.
My GPU does fine when training Tacotron alone.
Here's my nvcc:
NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:32_Central_Daylight_Time_2017
Cuda compilation tools, release 9.0, V9.0.176
Here's my output from "tf.__version__sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))":

tf.__version__sess=tf.Session(config=tf.ConfigProto(log_device_placement=True))
2018-07-03 09:52:41.754046: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-07-03 09:52:42.004040: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:01:00.0
totalMemory: 12.00GiB freeMemory: 9.93GiB
2018-07-03 09:52:42.011150: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0
2018-07-03 09:52:42.547125: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-03 09:52:42.550875: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929]      0
2018-07-03 09:52:42.553274: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0:   N
2018-07-03 09:52:42.555655: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9616 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0, compute capability: 6.1
2018-07-03 09:52:42.974984: I T:\src\github\tensorflow\tensorflow\core\common_runtime\direct_session.cc:284] Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0, compute capability: 6.1

@v-yunbin
Posted on StackOverflow (official tensorflow website forms): https://stackoverflow.com/questions/51256459/conv2dcustombackpropfilterop-only-supports-nhwc-error-tacotron-2
Currently waiting for a reply. Perhaps we should upvote it to try to get a higher priority? Feel free to add to the post as well.

Encountered the same issue. Tried running Wavenet training without changing anything in hparams.py, still is still occurred.

@v-yunbin, that's awesome to hear!
I'm still getting errors, so is there any chance you could share more details?
i.e:
*OS Platform and Distribution (e.g., Linux Ubuntu 16.04).
*TensorFlow installed from (source or binary).
*TensorFlow version (use command below).
*Python version (as well as from cmd or anaconda).
*CUDA/cuDNN version.
*GPU model and memory.
Thanks!

@avivelor
*OS Platform and Distribution:  CentOS Linux release 7.3.1611 (Core)
*TensorFlow installed from:   binary (use "pip" to  install it )
*TensorFlow version tensorflow-gpu 1.8 (use "pip" to  install it )
*Python version: anaconda 3.5
*CUDA/cuDNN version: cuda-9.0
*GPU model and memory: 1T

Had another go at this today. I nuked my python installation, installed and used Python 3.6 with virtualenv, installing only the requirements.txt and tensorflow-gpu 1.9.0, but the issue persists.
I ran python train.py --model="Both" --hparams="outputs_per_step=11,tacotron_batch_size=16"
It trained the tacotron model for a bit over a couple of hours, and crashed with the NHWC error at the "Wavenet Train" stage.
*OS Platform and Distribution: Windows 10 64bit
*TensorFlow installed from: pip install tensorflow-gpu
*TensorFlow version 1.9.0 (or 1.8.0)
*Python version: 3.6
*CUDA/cuDNN version: cuda-9.0 (or 9.2)
*GPU model and memory: Nvidia gtx 1080 8GB
I preprocessed my data using the LJSpeech default option
I also tried v1.8.0 of tensorflow-gpu, running --model="Wavenet" (and then tacotron_batch_size 8 and 4), to no avail.
Finally, I reinstalled CUDA drivers, updating to 9.2 (with batch sizes of 16 or 8), also to no avail.
At this point, I'm no longer sure if this has anything to do with dependencies.

I was not able to reproduce the Error no matter how hard I tried. (I even changed the Conv2Dtranspose to channels_last and couldn't reproduce.
My setup:

Ubuntu 16.04, 64-bit
Tensorflow-gpu built from source (tried with 1.7 and 1.8)
Python: 3.6 (anaconda)
CUDA/CUDNN: 9.0/7.1
GPU: Nvidia Titan Xp 12GB

I would ask from you guys to check that you don't have tensorflow (cpu) version installed in parallel on your machine, and try to build from source with exact same build I have.
Since this error doesn't seem to be coming from the repo, I am closing the issue. If you find any news, feel free to reopen.

It seems that the problem appear with  Windows 10 64bit

Hey, I got the same issue when I training the wavenet, Did you find the solution?Thanks very much……
#############################################################
Wavenet Train
###########################################################
Checkpoint_path: logs-Tacotron-2/wave_pretrained/wavenet_model.ckpt
Loading training data from: tacotron_output/gta/map.txt
Using model: Tacotron-2
Hyperparameters:
allow_clipping_in_normalization: True
attention_dim: 128
attention_filters: 32
attention_kernel: (31,)
cin_channels: 80
cleaners: english_cleaners
clip_mels_length: True
cross_entropy_pos_weight: 1
cumulative_weights: True
decoder_layers: 2
decoder_lstm_units: 1024
embedding_dim: 512
enc_conv_channels: 512
enc_conv_kernel_size: (5,)
enc_conv_num_layers: 3
encoder_lstm_units: 256
fmax: 7600
fmin: 0
frame_shift_ms: None
freq_axis_kernel_size: 3
gate_channels: 512
gin_channels: -1
griffin_lim_iters: 60
hop_size: 300
input_type: raw
kernel_size: 3
layers: 30
leaky_alpha: 0.4
log_scale_min: -32.23619130191664
log_scale_min_gauss: -16.11809565095832
mask_decoder: False
mask_encoder: False
max_abs_value: 4.0
max_iters: 1000
max_mel_frames: 1300
max_time_sec: None
max_time_steps: 13000
min_level_db: -100
n_fft: 2048
n_speakers: 5
natural_eval: False
normalize_for_wavenet: True
num_freq: 1025
num_mels: 80
out_channels: 2
outputs_per_step: 2
postnet_channels: 512
postnet_kernel_size: (5,)
postnet_num_layers: 5
power: 1.5
predict_linear: True
prenet_layers: [256, 256]
quantize_channels: 65536
ref_level_db: 20
rescale: True
rescaling_max: 0.999
residual_channels: 512
sample_rate: 24000
signal_normalization: True
silence_threshold: 2
skip_out_channels: 256
smoothing: False
stacks: 3
stop_at_any: True
symmetric_mels: False
tacotron_adam_beta1: 0.9
tacotron_adam_beta2: 0.999
tacotron_adam_epsilon: 1e-06
tacotron_batch_size: 8
tacotron_clip_gradients: False
tacotron_data_random_state: 1234
tacotron_decay_learning_rate: True
tacotron_decay_rate: 0.4
tacotron_decay_steps: 50000
tacotron_dropout_rate: 0.5
tacotron_final_learning_rate: 1e-05
tacotron_initial_learning_rate: 0.001
tacotron_random_seed: 5339
tacotron_reg_weight: 1e-06
tacotron_scale_regularization: True
tacotron_start_decay: 50000
tacotron_swap_with_cpu: False
tacotron_synthesis_batch_size: 128
tacotron_teacher_forcing_decay_alpha: 0.0
tacotron_teacher_forcing_decay_steps: 280000
tacotron_teacher_forcing_final_ratio: 0.0
tacotron_teacher_forcing_init_ratio: 1.0
tacotron_teacher_forcing_mode: constant
tacotron_teacher_forcing_ratio: 1.0
tacotron_teacher_forcing_start_decay: 10000
tacotron_test_batches: 12
tacotron_test_size: None
tacotron_zoneout_rate: 0.1
train_with_GTA: True
trim_fft_size: 512
trim_hop_size: 128
trim_silence: True
trim_top_db: 23
upsample_conditional_features: True
upsample_scales: [15, 20]
use_bias: True
use_lws: False
use_speaker_embedding: True
wavenet_adam_beta1: 0.9
wavenet_adam_beta2: 0.999
wavenet_adam_epsilon: 1e-08
wavenet_batch_size: 2
wavenet_data_random_state: 1234
wavenet_dropout: 0.05
wavenet_ema_decay: 0.9999
wavenet_learning_rate: 0.001
wavenet_random_seed: 5339
wavenet_swap_with_cpu: False
wavenet_synthesis_batch_size: 8
wavenet_test_batches: None
wavenet_test_size: 0.0441
win_size: 1200
Initializing Wavenet model.  Dimensions (? = dynamic shape):
Train mode:                True
Eval mode:                 False
Synthesis mode:            False
inputs:                    (?, 1, ?)
local_condition:           (?, 80, ?)
targets:                   (?, ?)
outputs:                   (?, ?)
Initializing Wavenet model.  Dimensions (? = dynamic shape):
Train mode:                False
Eval mode:                 True
Synthesis mode:            False
local_condition:           (1, 80, ?)
targets:                   (?,)
outputs:                   (?,)
Wavenet training set to a maximum of 1300000 steps
Generated 8 train batches of size 2 in 0.066 sec
Generated 578 test batches of size 1 in 1.062 sec
Generated 8 train batches of size 2 in 0.034 sec
Exiting due to exception: OOM when allocating tensor with shape[2,512,1,12900] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/ExpandDims_1)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
     [[Node: model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod/_979 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_5028_model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Caused by op 'model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D', defined at:
File "train.py", line 139, in
main()
File "train.py", line 127, in main
train(args, log_dir, hparams)
File "train.py", line 80, in train
checkpoint = wavenet_train(args, log_dir, hparams, input_path)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/train.py", line 251, in wavenet_train
return train(log_dir, args, hparams, input_path)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/train.py", line 175, in train
model, stats = model_train_mode(args, feeder, hparams, global_step)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/train.py", line 123, in model_train_mode
feeder.input_lengths, x=feeder.inputs)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/wavenet.py", line 176, in initialize
y_hat = self.step(x, c, g, softmax=False) #softmax is automatically computed inside softmax_cross_entropy if needed
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/wavenet.py", line 481, in step
x, h = conv(x, c, g_bct)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 277, in call
x, s, _ = self.step(x, c, g, False)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 326, in step
x = _conv1x1_forward(self.conv1x1_out, x, is_incremental)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 224, in _conv1x1_forward
return conv(x)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 165, in call
stride=1, padding='VALID', data_format='NWC')
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py", line 497, in new_func
return func(*args, **kwargs)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py", line 497, in new_func
return func(*args, **kwargs)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 2464, in conv1d
data_format=data_format)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 956, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[2,512,1,12900] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/ExpandDims_1)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
     [[Node: model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod/_979 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_5028_model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Traceback (most recent call last):
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1322, in _do_call
return fn(*args)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1307, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1409, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[2,512,1,12900] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/ExpandDims_1)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
     [[Node: model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod/_979 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_5028_model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/train.py", line 217, in train
step, y_hat, loss, opt = sess.run([global_step, model.y_hat, model.loss, model.optimize])
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 900, in run
run_metadata_ptr)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1316, in _do_run
run_metadata)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[2,512,1,12900] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/ExpandDims_1)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
     [[Node: model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod/_979 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_5028_model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Caused by op 'model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D', defined at:
File "train.py", line 139, in
main()
File "train.py", line 127, in main
train(args, log_dir, hparams)
File "train.py", line 80, in train
checkpoint = wavenet_train(args, log_dir, hparams, input_path)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/train.py", line 251, in wavenet_train
return train(log_dir, args, hparams, input_path)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/train.py", line 175, in train
model, stats = model_train_mode(args, feeder, hparams, global_step)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/train.py", line 123, in model_train_mode
feeder.input_lengths, x=feeder.inputs)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/wavenet.py", line 176, in initialize
y_hat = self.step(x, c, g, softmax=False) #softmax is automatically computed inside softmax_cross_entropy if needed
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/wavenet.py", line 481, in step
x, h = conv(x, c, g_bct)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 277, in call
x, s, _ = self.step(x, c, g, False)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 326, in step
x = _conv1x1_forward(self.conv1x1_out, x, is_incremental)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 224, in _conv1x1_forward
return conv(x)
File "/home/lls/sai/Tacotron-2_Rayhane-mamah/wavenet_vocoder/models/modules.py", line 165, in call
stride=1, padding='VALID', data_format='NWC')
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py", line 497, in new_func
return func(*args, **kwargs)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py", line 497, in new_func
return func(*args, **kwargs)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 2464, in conv1d
data_format=data_format)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 956, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def)
File "/home/lls/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[2,512,1,12900] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D = Conv2D[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer, model/inference/residual_block_out_conv_layer_ResidualConv1dGLU_1/conv1d/ExpandDims_1)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
     [[Node: model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod/_979 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_5028_model/inference/residual_block_conv_layer_ResidualConv1dGLU_11/mod", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Traceback (most recent call last):
File "train.py", line 139, in
main()
File "train.py", line 127, in main
train(args, log_dir, hparams)
File "train.py", line 82, in train
raise ('Error occured while training Wavenet, Exiting!')
TypeError: exceptions must derive from BaseException

Hey, I got the same issue when I training the wavenet, Did you find the solution?Thanks very much……

I fixed it by switching over to Ray's Setup.
My setup:

Ubuntu 16.04, 64-bit (HWE installed from server)
Tensorflow-gpu built from source (tried with 1.7 and 1.8)
Python: 3.6 (anaconda)
CUDA/CUDNN: 9.0/7.1
GPU: Nvidia GTX 1080

On Tue, Oct 9, 2018, 6:57 PM CherryCloris<EMAIL_ADDRESS>wrote:

Hey, I got the same issue when I training the wavenet, Did you find the
solution?Thanks very much……
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/Rayhane-mamah/Tacotron-2/issues/73#issuecomment-428411542,
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.

It's resolved ..
the issue is because of image_data_format the backend(Tensorflow/Theano/CNTK...) uses
if you are using Keras  (backend as Tensorflow) the data_format='channels_last'  with images reshaped
to  (batch_size,row_size,column_size,channel_size) would solve the issue

@lucy3589  Why tensorflow-cpu cause this problem?

It's resolved ..
the issue is because of image_data_format the backend(Tensorflow/Theano/CNTK...) uses
if you are using Keras (backend as Tensorflow) the data_format='channels_last' with images reshaped
to (batch_size,row_size,column_size,channel_size) would solve the issue

It worked !!!!

已解决..
问题是由于image_data_format后端（Tensorflow / Theano / CNTK ...）使用的原因，
如果您使用Keras（后端为Tensorflow），则data_format ='channels_last'的图像被重塑
为（batch_size，row_size，column_size， channel_size）将解决问题

有效 ！！！！

I'm a newbie. Can you be more specific?thank you

@PrasannaKumarARDS

It's resolved ..
the issue is because of image_data_format the backend(Tensorflow/Theano/CNTK...) uses
if you are using Keras (backend as Tensorflow) the data_format='channels_last' with images reshaped
to (batch_size,row_size,column_size,channel_size) would solve the issue

can you explain this a bit more?
Where do you find data_format='channels_last' ?
In what file does this change need to be made?

@PrasannaKumarARDS

It's resolved ..
the issue is because of image_data_format the backend(Tensorflow/Theano/CNTK...) uses
if you are using Keras (backend as Tensorflow) the data_format='channels_last' with images reshaped
to (batch_size,row_size,column_size,channel_size) would solve the issue

can you explain this a bit more?
Where do you find data_format='channels_last' ?
In what file does this change need to be made?

the data_format  is an argument you can specify in the  keras.layers.Conv1D()

@PrasannaKumarARDS @rameshKrSah Do you know if how this could be implemented into a fork of this project? I'm using karamarieliu/gst_tacotron2_wavenet which is this project but with Tacotron 2 GST. Wavenet on the repo is similar to this repo's earlier 2018 modules.py.
I tried changing the ConvTranspose2d data_format to last but got this error.
Traceback (most recent call last):
  File "train.py", line 127, in <module>
    main()
  File "train.py", line 119, in main
    wavenet_train(args, log_dir, hparams, args.wavenet_input)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 244, in wavenet_train
    return train(log_dir, args, hparams, input_path)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 167, in train
    model, stats = model_train_mode(args, feeder, hparams, global_step)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 117, in model_train_mode
    feeder.input_lengths, x=feeder.inputs)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\models\wavenet.py", line 169, in initialize
    y_hat = self.step(x, c, g, softmax=False) #softmax is automatically computed inside softmax_cross_entropy if needed
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\models\wavenet.py", line 435, in step
    c = transposed_conv(c)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\models\modules.py", line 333, in __call__
    return self.convt(inputs)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\layers\base.py", line 362, in __call__
    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 728, in __call__
    self.build(input_shapes)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\keras\layers\convolutional.py", line 719, in build
    raise ValueError('The channel dimension of the inputs '
ValueError: The channel dimension of the inputs should be defined. Found `None`.

but with it as the default, I get this
2020-03-18 13:38:25.399613: E T:\src\github\tensorflow\tensorflow\core\common_runtime\executor.cc:697] Executor failed to create kernel. Invalid argument: Conv2DCustomBackpropFilterOp only supports NHWC.
         [[Node: model/optimizer/gradients/model/inference/conv2d_transpose_1/conv2d_transpose_grad/Conv2DBackpropFilter = Conv2DBackpropFilter[T=DT_FLOAT, _class=["loc:@model/optimizer/clip_by_global_norm/mul_199"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 16], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/optimizer/gradients/model/inference/conv2d_transpose_1/BiasAdd_grad/tuple/control_dependency/_2171, model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Shape, model/inference/conv2d_transpose/BiasAdd/_2173)]]
Exiting due to Exception: Conv2DCustomBackpropFilterOp only supports NHWC.
         [[Node: model/optimizer/gradients/model/inference/conv2d_transpose_1/conv2d_transpose_grad/Conv2DBackpropFilter = Conv2DBackpropFilter[T=DT_FLOAT, _class=["loc:@model/optimizer/clip_by_global_norm/mul_199"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 16], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/optimizer/gradients/model/inference/conv2d_transpose_1/BiasAdd_grad/tuple/control_dependency/_2171, model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Shape, model/inference/conv2d_transpose/BiasAdd/_2173)]]

Caused by op 'model/optimizer/gradients/model/inference/conv2d_transpose_1/conv2d_transpose_grad/Conv2DBackpropFilter', defined at:
  File "train.py", line 127, in <module>
    main()
  File "train.py", line 119, in main
    wavenet_train(args, log_dir, hparams, args.wavenet_input)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 244, in wavenet_train
    return train(log_dir, args, hparams, input_path)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 167, in train
    model, stats = model_train_mode(args, feeder, hparams, global_step)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 119, in model_train_mode
    model.add_optimizer(global_step)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\models\wavenet.py", line 365, in add_optimizer
    gradients, variables = zip(*optimizer.compute_gradients(self.loss))
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\training\optimizer.py", line 514, in compute_gradients
    colocate_gradients_with_ops=colocate_gradients_with_ops)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 596, in gradients
    gate_gradients, aggregation_method, stop_gradients)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 779, in _GradientsHelper
    lambda: grad_fn(op, *out_grads))
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 398, in _MaybeCompile
    return grad_fn()  # Exit early
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 779, in <lambda>
    lambda: grad_fn(op, *out_grads))
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\nn_grad.py", line 54, in _Conv2DBackpropInputGrad
    data_format=op.get_attr("data_format")),
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1190, in conv2d_backprop_filter
    dilations=dilations, name=name)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\util\deprecation.py", line 454, in new_func
    return func(*args, **kwargs)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\framework\ops.py", line 3155, in create_op
    op_def=op_def)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\framework\ops.py", line 1717, in __init__
    self._traceback = tf_stack.extract_stack()

...which was originally created as op 'model/inference/conv2d_transpose_1/conv2d_transpose', defined at:
  File "train.py", line 127, in <module>
    main()
[elided 2 identical lines from previous traceback]
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 167, in train
    model, stats = model_train_mode(args, feeder, hparams, global_step)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\train.py", line 117, in model_train_mode
    feeder.input_lengths, x=feeder.inputs)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\models\wavenet.py", line 169, in initialize
    y_hat = self.step(x, c, g, softmax=False) #softmax is automatically computed inside softmax_cross_entropy if needed
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\models\wavenet.py", line 435, in step
    c = transposed_conv(c)
  File "C:\Users\camja\Desktop\gst_tacotron2_wavenet\wavenet_vocoder\models\modules.py", line 333, in __call__
    return self.convt(inputs)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\layers\base.py", line 362, in __call__
    outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 736, in __call__
    outputs = self.call(inputs, *args, **kwargs)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\keras\layers\convolutional.py", line 781, in call
    data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1254, in conv2d_transpose
    name=name)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1340, in conv2d_backprop_input
    dilations=dilations, name=name)
  File "C:\Users\camja\Anaconda3\envs\taco\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)

InvalidArgumentError (see above for traceback): Conv2DCustomBackpropFilterOp only supports NHWC.
         [[Node: model/optimizer/gradients/model/inference/conv2d_transpose_1/conv2d_transpose_grad/Conv2DBackpropFilter = Conv2DBackpropFilter[T=DT_FLOAT, _class=["loc:@model/optimizer/clip_by_global_norm/mul_199"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 16], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/optimizer/gradients/model/inference/conv2d_transpose_1/BiasAdd_grad/tuple/control_dependency/_2171, model/optimizer/gradients/model/inference/conv2d_transpose/conv2d_transpose_grad/Shape, model/inference/conv2d_transpose/BiasAdd/_2173)]]

Am I missing something?

It's resolved ..
the issue is because of image_data_format the backend(Tensorflow/Theano/CNTK...) uses
if you are using Keras (backend as Tensorflow) the data_format='channels_last' with images reshaped
to (batch_size,row_size,column_size,channel_size) would solve the issue

yes!! I find it useful

Hi guys,
I'm trying to get WaveNet training working and I keep getting this problem. I can't find its location and don't know how to fix it. Or is there an update on this issue? I only have tansorflow-gpu installed. data_format='channels_last' doesn't work or I don't understand where to change it. Tacotron workout went through without any problems.
Exiting due to exception: Conv2DCustomBackpropInputOp only supports NHWC.
[[node WaveNet_model/optimizer_1/gradients/WaveNet_model/inference/final_convolution_2/final_convolution_2_1/final_convolution_2/conv1d/Conv2D_grad/Conv2DBackpropInput (defined at /notebooks/Tacotron-2/wavenet_vocoder/models/wavenet.py:557) = Conv2DBackpropInput[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](WaveNet_model/optimizer_1/gradients/WaveNet_model/inference/final_convolution_2/final_convolution_2_1/final_convolution_2/conv1d/Conv2D_grad/ShapeN, WaveNet_model/inference/final_convolution_2/final_convolution_2_1/final_convolution_2/conv1d/ExpandDims_1, WaveNet_model/optimizer_1/gradients/WaveNet_model/inference/final_convolution_2/final_convolution_2_1/final_convolution_2/conv1d/Squeeze_grad/Reshape)]]
Caused by op 'WaveNet_model/optimizer_1/gradients/WaveNet_model/inference/final_convolution_2/final_convolution_2_1/final_convolution_2/conv1d/Conv2D_grad/Conv2DBackpropInput', defined at:
File "train.py", line 138, in
main()
File "train.py", line 130, in main
wavenet_train(args, log_dir, hparams, args.wavenet_input)
File "/notebooks/Tacotron-2/wavenet_vocoder/train.py", line 346, in wavenet_train
return train(log_dir, args, hparams, input_path)
File "/notebooks/Tacotron-2/wavenet_vocoder/train.py", line 230, in train
model, stats = model_train_mode(args, feeder, hparams, global_step)
File "/notebooks/Tacotron-2/wavenet_vocoder/train.py", line 178, in model_train_mode
model.add_optimizer(global_step)
File "/notebooks/Tacotron-2/wavenet_vocoder/models/wavenet.py", line 557, in add_optimizer
gradients = optimizer.compute_gradients(self.tower_loss[i])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/optimizer.py", line 519, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 630, in gradients
gate_gradients, aggregation_method, stop_gradients)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 814, in _GradientsHelper
lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 408, in _MaybeCompile
return grad_fn() # Exit early
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 814, in
lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_grad.py", line 517, in _Conv2DGrad
data_format=data_format),
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 1229, in conv2d_backprop_input
dilations=dilations, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1770, in init
self._traceback = tf_stack.extract_stack()
...which was originally created as op 'WaveNet_model/inference/final_convolution_2/final_convolution_2_1/final_convolution_2/conv1d/Conv2D', defined at:
File "train.py", line 138, in
main()
[elided 2 identical lines from previous traceback]
File "/notebooks/Tacotron-2/wavenet_vocoder/train.py", line 230, in train
model, stats = model_train_mode(args, feeder, hparams, global_step)
File "/notebooks/Tacotron-2/wavenet_vocoder/train.py", line 176, in model_train_mode
feeder.input_lengths, x=feeder.inputs)
File "/notebooks/Tacotron-2/wavenet_vocoder/models/wavenet.py", line 277, in initialize
y_hat_train = self.step(tower_x[i], tower_c[i], tower_g[i], softmax=False) #softmax is automatically computed inside softmax_cross_entropy if needed
File "/notebooks/Tacotron-2/wavenet_vocoder/models/wavenet.py", line 719, in step
x = conv(x)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 757, in call
outputs = self.call(inputs, *args, **kwargs)
File "/notebooks/Tacotron-2/wavenet_vocoder/models/modules.py", line 382, in call
return super(Conv1D1x1, self).call(inputs, incremental=incremental, convolution_queue=convolution_queue)
File "/notebooks/Tacotron-2/wavenet_vocoder/models/modules.py", line 319, in call
outputs = self.layer.call(inputs_)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 384, in call
return super(Conv1D, self).call(inputs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 194, in call
outputs = self._convolution_op(inputs, self.kernel)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 868, in call
return self.conv_op(inp, filter)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 520, in call
return self.call(inp, filter)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 204, in call
name=self.name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 193, in _conv1d
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 553, in new_func
return func(*args, **kwargs)


about dubbo support?

是不是目前 brave-instrumentation-dubbo-rpc还不支持通过ExtraFieldPropagation传参？

ExtraFieldPropagation is added in the Tracing object, not dubbo directly. So, if you configure it there, dubbo should take it. I think dubbo gets Tracing object from ExtensionFactory or spring.
Please come to gitter for more questions https://gitter.im/openzipkin/zipkin

i was created ExtensionFactory and created spring bean of Tracing.but i get params was null. that's my coding.https://github.com/1991wangliang/sleuth-webmvc-example


how to customize sound of keyboard buttons ?

You can take a look at the Feedback namespace.

thank you!

can you please give me an example of how can I change keyboard default sound. I read the documentation and it says just simply use trigger method. So i added this line to a button response, but it still plays default audio. please check:
Button("Show Sheet") { AudioFeedback.custom(id: 50).trigger() }

If you look at the code for AudioFeedback, AudioFeedbackEngine and StandardAudioFeedbackEngine you can see that the audio feedback enum just specifies system audio IDs with some name aliases, and the StandardAudioFeedbackEngine just plays the ID of the audio. So it really should play the ID you provide it with.

Thank you for your kind response. I see these classes but its confusing for me little. can you you give me an example code ?I want to change default keyboard audio of all buttons.

Button("play custom audio"){ StandardAudioFeedbackEngine.shared.trigger(AudioFeedback.custom(id: 1125)) }
This is working for me now. But I want this sound to be the default keyboard audio. I'm trying to modify the handler as follows:
override func triggerFeedback(for gesture: KeyboardGesture, on action: KeyboardAction) {
StandardAudioFeedbackEngine.shared.trigger(AudioFeedback.custom(id: 1125))
}
but its not working.. please help!

I don't have time to look at this now, but I can re-open the issue and see if anyone else helps.
Also, if you solve it, please add that information here to help others that may have the same problems as you 👍

okay thanks. I'm trying and lets see if anyone responds.. fingers crossed.

` // MyKeyboardFeedbackHandler.swift
class MyKeyboardFeedbackHandler: StandardKeyboardFeedbackHandler {
override func triggerFeedback(for gesture: KeyboardGesture, on action: KeyboardAction) {
    return AudioFeedback.none.trigger()
}

}
// KeyboardViewController.swift
keyboardFeedbackHandler = MyKeyboardFeedbackHandler(settings: KeyboardFeedbackSettings())
super.viewDidLoad()
`
Is the above correct ? seems okay to me, but still not working. :(

@danielsaidi sir can you please give me a hint. please.

@maazali30 I can take a look when I'm back from my travel. Until then, try to debug and understand what's happening. What sound ID is really played when your custom one fails to play. and why is that?

Hi @maazali30
I have looked into this now, and the easiest way is to just configure the keyboardFeedbackSettings property of the keyboard input controller, for instance:
func setupFeedback() {
    let useAudio = keyboardSettings.isAudioFeedbackEnabled
    let useHaptics = keyboardSettings.isHapticFeedbackEnabled
    keyboardFeedbackSettings.audioConfiguration = useAudio ? .enabled : .noFeedback
    keyboardFeedbackSettings.audioConfiguration.input = .custom(id: 1329)
    keyboardFeedbackSettings.hapticConfiguration = useHaptics ? .enabled : .noFeedback
}

This will automatically be applied to the feedback handler user by the controller.


Unexpected decoding errors when using forProduct* decoders and optional fields

Consider this code:
import io.circe._
import io.circe.generic.auto._

object Test extends App {
  case class First(_notify: Option[Second])
  case class Second(value: Option[Vector[String]])

  implicit val firstDecoder: Decoder[First] = Decoder.forProduct1("notify")(First.apply)

  val input =
    """
      |{
      |  "notify": {
      |    "value": ["a", "b"]
      |  }
      |}
    """.stripMargin

  val json = parser.parse(input).fold(throw _, identity)
  println(json.as[First].fold(throw _, identity))
}

This code fails with the following exception:
Exception in thread "main" DecodingFailure(CNil, List(DownField(value), DownField(notify)))

If I remove either option in the case classes definitions,, or if I avoid forProduct1 and rely on the automatic derivation (although in this case the JSON input has to be changed appropriately), then everything works as expected. Funny thing, if I add an explicit decoder with forProduct1 for Second, I get a NullPointerException:
Exception in thread "main" java.lang.NullPointerException
	at io.circe.Decoder$$anonfun$decodeOption$1.apply(Decoder.scala:652)
	at io.circe.Decoder$$anonfun$decodeOption$1.apply(Decoder.scala:650)
	at io.circe.Decoder$$anon$27.tryDecode(Decoder.scala:341)
	at io.circe.ACursor.as(ACursor.scala:336)
	at io.circe.ACursor.get(ACursor.scala:343)
	at io.circe.ProductDecoders$$anon$1.apply(ProductDecoders.scala:12)
	at io.circe.Json.as(Json.scala:97)

This bug affected me when I tried to map the notify field in the input JSON object to the _notify field in my own case class, while keeping everything else derived automatically.

@netvl What version are you on? That code works as written for me on both 0.8.0 and 0.9.0-M1, and I'm not sure what could cause that to happen on earlier versions.
For the NPE, I'm guessing the issue involves the initialization order of vals in an App object, but if you can post a reproduction I'm happy to take a look.

@netvl Adding an explicit decoder for second works for me:
implicit val secondDecoder: Decoder[Second] = Decoder.forProduct1("value")(Second.apply)
implicit val firstDecoder: Decoder[First] = Decoder.forProduct1("notify")(First.apply)

Indeed, this is strange. I tried running the example again, and it worked. And yes, the NPE thing certainly did happen because I used val instead of lazy val.


pipeline v1 automation -- create, run, delete

added delete test case
moved pipeline delete to main test
ran flake8, added environment variables
added clarifying comments

closing in favor of #31595


feat(es/transforms/compat): Add loose mode to parameters

closes #2825 #2800

I would need some help @kdy1
The test case of issue_2811 is failing because it inject _this to wrong scope, so the result will be something like
var f = function () { ... }
if (true) {
} else {
   var _this = this
}

Why is this happening? I guess it's because of Parallel, how does that work and how should I fix it?

#[parallel] only works if the number of statements (at the same level) is more than 64, so I don't think it's the case.
I think visit_mut_stmts should store some 'shared' variables like self.vars to somewhere, because otherwise variables from different statements will be injected by visit_mut_stmts.

var f = function () { ... }
if (true) {
} else {
   var _this = this
}

for this case, visit_mut_stmts for the top level generates variable, and visit_mut_stmts for
{
   var _this = this
}

takes the variables, just as those variables are generated by the nested block.

But parallel doesn't allow a custom visit_mut_stmts, what should I do?

You can remove #[parallel] for now. I'll modify #[parallel] in future.


Link to "Details" modal

Hello,
This is an enhancement request. We are currently exploring predictions, and currently it's hard to refer to rules linked to a prediction other than something like "the fourth from the top" or with a screen capture.
Could you add shareable links to these modals?
Thanks in advance!

Hi,
I am not sure if I understand your problem correctly, all links should be sharable, also the list of top-k predictions, like f.e.:
http://explore.ai-strategies.org/wn18rr/max/task?taskID=2044
Or if you want to link a specific prediction f.e. "the fourth from the top":
http://explore.ai-strategies.org/wn18rr/max/explanation?taskID=2044&entityID=2139

Hi Simon,
Thanks for looking into it!
Actually, I would like a direct link on the rules details, this icon.
[Diagram, application  Description automatically generated]
Would that be possible?
Cheers,
Fred
From: Simon Ott @.>
Date: Tuesday, 23 August 2022 at 17:54
To: OpenBioLink/Explorer @.>
Cc: Frederic Burdet @.>, Author @.>
Subject: Re: [OpenBioLink/Explorer] Link to "Details" modal (Issue #20)
Hi,
I am not sure if I understand your problem correctly, all links should be sharable, also the list of top-k predictions, like f.e.:
http://explore.ai-strategies.org/wn18rr/max/task?taskID=2044
Or if you want to link a specific prediction f.e. "the fourth from the top":
http://explore.ai-strategies.org/wn18rr/max/explanation?taskID=2044&entityID=2139
—
Reply to this email directly, view it on GitHubhttps://github.com/OpenBioLink/Explorer/issues/20#issuecomment-1224267924, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ACSOZWPTW5I6IQN7E4WBRVLV2TX2VANCNFSM57LVRFNQ.
You are receiving this because you authored the thread.Message ID: @.***>


bug(MENU): Types of property 'hasBackdrop' are incompatible

Is this a regression?

[X] Yes, this behavior used to work in the previous version

The previous version in which this bug was not present was
Definitely worked in 12.2.9, didn't check with later versions
Description
On version 13.1.0 with exactOptionalPropertyTypes flag,
when trying to use the Menu component, following error shows up in Terminal:

Reproduction
StackBlitz Example project doesn't build on latest version, so I couldn't reproduce it for you. You can see it working on v12: https://stackblitz.com/edit/components-issue-fau7sh?file=src%2Fapp%2Fexample-component.html.
To check locally, create a new Angular 13 project, install the material library and enable the exactOptionalPropertyTypes flag in tsconfig.json
Expected Behavior
Code compiles and Menu works.
Actual Behavior
Code doesn't compile, Menu doesn't work.
Environment

Angular: 13.1.0
CDK/Material: 13.1.0
Browser(s): Chrome 96
Operating System: Windows 11

If you guys have any questions, let me know

We can try adding this flag to our dev-app and observing places where this fails

I took a quick look at this and there are many more places that fail due to the compiler flag. I have a WIP branch, but it might take a while to work through all the failures.


Add raytracing in one weekend

What does this PR do?
Add resource(s)
For resources
Description
Adds a complete tutorial for ray tracing in any language, though the books use c++.
Why is this valuable (or not)?
For those who want to learn ray tracing from scratch, without knowledge of a specific language. I did it in rust!
How do we know it's really free?
Taken from the site:

The Ray Tracing in One Weekend series of books are now available to the public for free online. They are now released under the CC0 license. This means that they are as close to public domain as we can get. (While that also frees you from the requirement of providing attribution, it would help the overall project if you could point back to this web site as a service to other users.)

For book lists, is it a book? For course lists, is it a course? etc.
It's a book.
Checklist:

[x] Read our contributing guidelines
[x] Search for duplicates.
[x] Include author(s) and platform where appropriate.
[x] Put lists  in alphabetical order, correct spacing.
[x] Add needed indications (PDF, access notes, under construction)

Followup

Check the output of Travis-CI for linter errors!

Thanks, and welcome to free-programming-books!


Enable search based on usecase

Enable usecase search.
Eg. EBiSC
If we search EBiSC in search box, it should return all the resources related to EBiSC.

But search is case sensitive


WIP: Rudimentary text + URL + title searching.

@bgrins, @ncalexan: save some page content from the menu and try this out?

It's expected not to pass lint yet, because we do dangerous things.

This is looking pretty great!


Update test-and-docs.md

Proposed changes

Unreleased project version (optional)

Related issues (optional)

Thanks, @scjane :)


Sentinel Proofpoint Function App - Warning: ProofpointPOD_message_CL

Describe the bug
We are using the Sentinel proofpoint function app, warnings in the Sentinel logs - "The following fields' values msgParts of type ProofpointPOD_message_CL " & "The following fields' values msgParts of type ProofpointPOD_message_CL have been trimmed to the max allowed size, 32766 bytes. Please adjust your input accordingly."
To Reproduce
Steps to reproduce the behavior:
Sentinel Query:
"""
_LogOperation
| limit 1000
"""

Hi @justint-mpg, We have already got this issue and this consider as feature request for the connector. Our product team will take this request into their queue. We will notify you once the request has been completed.
So, closing this issue from GitHub. If you still need support for this issue, feel free to re-open it any time. Thank you for your co-operation.


Open Shift Request Approving in Reverse Sequence

When more than 1 request from different users for the same open shift occurs in-between runs the Approve an Open Shift Request action approves the newest request and denies the older requests. Essentially a Last In First Out method.  Seems that it should be First In First Out so the first request gets approved.
Is there a way to filter the list of open shift requests and make it approve in the order the request come in from first to last?

@4computing Thanks for calling out this scenario, I'm not sure if it's currently possible. But we're working on an approach to make a FIFO based approval process work.

@4computing We have now added triggers to notify you when a new request is created/updated/deleted. You could use this trigger to detect as soon as a request is created, and approve it immediately in a FIFO manner.
We're in the process of adding templates into Power Automate to help with setting this up.


Visual corruption in DOOM Eternal

As the title says, there are visual issues in DOOM Eternal. They occur with both AMDVLK and pro but they appear much quicker and more often with AMDVLK.  With the pro driver I can sometimes play for hours without this issue appearing but with the open driver it appears after 20-30 minutes at most. This issue does not occur with RADV.

I have the same issue, except mine also extends to many splash graphics, such as when starting any Challenge and the ripping of a heart happens.

This no longer happens for me with 2024.Q1.3-1


Ability to provide varying snap handle transform info based on controller type

Currently when applying a snap handle to a Interactable Object, it will only be at the correct orientation based on one controller.
However, the Vive wand in SteamVR is a different orientation to the Oculus Touch on home (and also different to Oculus Touch on SteamVR).
There needs to be a way that you can specify a snap handle but with different transform position/rotations based on the current controller being used.

Similar to: https://github.com/thestonefox/VRTK/issues/876
and has links to: https://github.com/thestonefox/VRTK/issues/472

As I mentioned on Slack:
We probably need a generic solution for per-SDK/headset settings. Someone wanted different activation buttons, and I'm sure there are other valid settings, too.

What do you mean different activation buttons?
You mean like on a pointer for instance at the moment you can say touchpad press activates? but you may want to say:
vive = touchpad press
oculus touch = grip press
?

The use case in Slack was teleportation:

Vive uses touchpad press
Rift should use touchpad touch

If it's only snap handles and button mapping I guess we're fine with this issue and #842.

@bddckr yeah I think that other issue is how we solve that (who does it first, VRTK or xR Foundation Toolkit?!)


Problem with training on KITTI

Hi,
I started training PIXOR your implementation with a Batch_size=1.
I trained upto 40 epoch as mentioned in the code. Loaded the checkpoint 34 from trained model.
It seems that after training num_pred=0 i.e number of predictions boxes becomes zero.
Loss curves are behaving well.
Kindly provide solution if you have solved it.

Hi, do you know what dataset is used exactly? There are many parts of the KITTI Bird's Eye View dataset. I don't know which should I download. Thank you.
Bird's Eye View Evaluation 2017:
Download left color images of object data set (12 GB)
Download right color images, if you want to use stereo information (12 GB)
Download the 3 temporally preceding frames (left color) (36 GB)
Download the 3 temporally preceding frames (right color) (36 GB)
Download Velodyne point clouds, if you want to use laser information (29 GB)
Download camera calibration matrices of object data set (16 MB)
Download training labels of object data set (5 MB)


Support for Swift 2.2

[ ] Create swift2.1 branch.
[ ] Fix code for Swift 2.2.
[ ] Update README.md.

Note:

Swift2.2 → v1.2.0
〜Swift2.1 → v1.1.2

Open project in Xcode7.3 and found no warning,errors.
So, no need to support for Swift2.2


Do you plan on supporting the 1.5.x branch?

I am interested in seeing some bugs fixed in the 1.5.x branch (I am happy to provide the fixes if needed), but first I'd like to know whether swagger 2.0 support is going to continue or it the plan going forward is to only support swagger 3.0

@edrevo Apologies for the much delayed reply, and it's a very fair question.
While we are focused on OAS3 support, we plan on maintaining the 1.5.X branch for a while. It's a bit of a challenge keeping the balance between the two versions, but we realize there are still issues that need to be addressed in 1.5 and we do not expect people to migrate to 2.0 quickly. Of course, at some point we won't push out new versions  for 1.5, but we're not there yet.
Feel free to submit PRs for issues that are relevant to you, and we'll do our best to review in a timely manner (not like this ticket) and when there's enough sum of things to release, will push out a new version.


Fixes WebClient which started a client span prior to a subscription

In looking at underlying HttpClient mechanics, I noticed the reactor
call doesn't happen until subscribe. Before this change, we started the
client span at the ExchangeFilterFunction, not at subscribe time.

@simonbasle @violetagg before I go back and fixup test cases, does what I just did make sense with your understanding of WebClient and HttpClient?

I've looked at other code and decided that this doesn't require second eyes. there's no intent to stat a client request before the mono is subscribed to, and so we shouldn't do similar for the client span.


[iOS][ScrollView] Add allowScrollOutOfBounds to ScrollView

When using the scrollTo() function the user can specify a x/y position
that tells the ScrollView to scroll to. Prior to this patch the x/y were
not validated which could cause the SrollView to scroll to a position
outside the content view bounds, leaving the screen empty (the content
disapears, since it goes outside the screen). In order to fix this problem,
this commit imposes that the x/y must be inside the view bounds if allowScrollOutOfBounds
is set to false. This new prop is also set to true automatically to not break
existing apps which may depends on this feature.
Note that this behavior does not occur on Android, because the
Android's framework itself implements this features via 1 and 2, which are used
by React Native.
Changelog:
[iOS] [Added] - Add allowScrollOutOfBounds to ScrollView
Test Plan:
Run the following code on a iOS simulator/device and play around with the new allowScrollOutOfBounds property.

When set to false the scrollToOffset will stop at the last item (the item 12)
When set to true the scrollToOffset will scroll outside the content bounds and leave the screen completely white.

import React, {Component} from 'react';
import { StyleSheet, Text, View, FlatList, SafeAreaView } from 'react-native';

const data = [
  { key: "1" },
  { key: "2" },
  { key: "3" },
  { key: "4" },
  { key: "5" },
  { key: "6" },
  { key: "7" },
  { key: "8" },
  { key: "9" },
  { key: "10" },
  { key: "11" },
  { key: "12" }
];

export default class App extends Component {
  componentDidMount() {
    this.timeoutID = setTimeout(() => {
      if (this.flatList) {
        console.log('sdsadsadasdads');
        this.flatList.scrollToOffset({
          offset: 10000,
          animated: true
        });
      }
      this.timeoutID = null;
    }, 2000);
  }

  componentWillUnmount() {
    clearTimeout(this.timeoutID);
  }

  renderItem({ item: { key }}) {
    return (
      <View style={styles.view}>
        <Text style={styles.text}>{key}</Text>
      </View>
    )
  }

  onRef = (flatList) => {
    this.flatList = flatList;
  };

  timeoutID = null;
  flatList = null;

  render() {
    return (
      <SafeAreaView>
        <FlatList
          allowScrollOutOfBounds={false} // NOTE THIS NEW FLAG
          ref={this.onRef}
          style={styles.container}
          data={data}
          renderItem={this.renderItem}
        />
      </SafeAreaView>
    );
  }
}

const styles = StyleSheet.create({
  container: {
    paddingTop: 20,
    backgroundColor: '#F5FCFF',
  },
  view: {
    marginBottom: 10,
    justifyContent: 'center',
    alignItems: 'center',
    width: '100%',
    backgroundColor: 'green',
    height: 100,
  },
  text: {
    color: 'red',
    fontSize: 20,
  },
});

GIF demo
Without the patch

With the patch

fixes #22768

If I understand correctly, you are changing scrollTo so that if you try to scrollTo a location outside of the bounds of the scrollview it will be bounded to the size of the scrollview. And you are adding this prop so as to not change the current behavior?
I'm not very familiar with ScrollView behavior but I'm curious if the current behavior would be seen as a bug that we should just change without adding the additional prop. Maybe @sahrens or @wcandillon have some thoughts.

Hello @TheSavior, yes you're correct. I'm only adding this prop to do not break the current behavior in iOS in case someone depends on this. But if you guys think it's not necessary I'll be glad to remove it.

I was actually going to open an issue/PR to prevent this same programatic overscroll behaviour.
I don't think RN should expose a prop to keep the "existing" behaviour. This behaviour sounds entirely like a bug, especially since the first user interaction causes a rubber-band effect & the behaviour differs from Android (consistent behaviour should be used wherever possible between platforms)
I can't speak for Facebook on this matter, but I'd highly suggest taking this PR without the allowScrollOutOfBounds flag

@berickson1 I'm happy to remove the flag. I just to know what Facebook has to say

@cabelitos can you rebase this PR and remove the allowScrollOutOfBounds?

@cpojer as a matter of fact a fix for this was already merged 1. This is soo sad, because I have this PR sitting here for months and that other PR was merged even though it was newer than mime.

 I'm so sorry about that. Up until recently we didn't have the bandwidth to take care of all contributions. We recently worked through 600 Pull Requests (See https://facebook.github.io/react-native/blog/2019/03/01/react-native-open-source-update ) and are now much more active on the React Native repo. As you can see, the number of open Pull Requests is less than 50, which is much more manageable than it was in the past :) Again, I'm so sorry this happened but I'm hoping to see your contributions again in the future, and I'll make sure to provide feedback quickly and get your changes merged.

@cpojer np at all, these things happens specially when you have 500+ PRs. I have another PR open which fixes problems for ART on Android. I hope you guys are able to take a look into it!

@cabelitos Ahha, I'm sorry, I didn't see this PR when I linked your bug report. And I see your fix now, we try to fix in different way, you add an option to enable/disable overflow, maybe you can try to create a new PR to let react native team for review.

@zhongwuzw we agreed that your solution is fine, and that we don't want to add a new flag for this.

@cpojer Get it ! :+1:
@cabelitos Apologize again! :heart:

@zhongwuzw we agreed that your solution is fine, and that we don't want to add a new flag for this.

So there is no chance of adding a new flag for this to re-enable the old behavior? We were using the behavior quite extensively as an alternative way to position TextInputs/Dropdowns/etc on the screen when selected/focused and oftentimes that caused some over scroll of the content.

@zhongwuzw we agreed that your solution is fine, and that we don't want to add a new flag for this.

@cpojer So there is no chance of adding a new flag for this to re-enable the old behavior? We were using the behavior quite extensively as an alternative way to position TextInputs/Dropdowns/etc on the screen when selected/focused and oftentimes that caused some over scroll of the content.

I agree with you, I used this old behavior  to position TextInputs in previous projects, but now,I have to edit the native code to use the old behavior

@sheepmiee Hey, flag already be added in https://github.com/facebook/react-native/pull/24296, please see that.


electron-server crashing

While rendering a large PDF, using "electron-server" strategy jsreport crashed with error.
When I switched to "electron-ipc" or "dedicated-process", the PDF is rendered in about 4 minutes, with 2,498 pages...
I am wanting to use "electron-server" as documentation says that it has most performance....
here is the error when it crashed using "electron-server"
2016-11-16T20:53:47.638Z - error: Error when processing render request Error during rendering report: socket hang up Error: socket hang up
    at createHangUpError (_http_client.js:252:15)
    at Socket.socketOnEnd (_http_client.js:344:23)
    at emitNone (events.js:91:20)
    at Socket.emit (events.js:185:7)
    at endReadableNT (_stream_readable.js:974:12)
    at _combinedTickCallback (internal/process/next_tick.js:74:11)
    at process._tickCallback (internal/process/next_tick.js:98:9)
From previous event:
    at X:\Projects\jsreport\node_modules\jsreport-core\lib\render\render.js:144:54
    at _fulfilled (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:794:54)
    at self.promiseDispatch.done (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:823:30)
    at Promise.promise.promiseDispatch (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:756:13)
    at X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:564:44
    at flush (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:110:17)
    at _combinedTickCallback (internal/process/next_tick.js:67:7)
    at process._tickCallback (internal/process/next_tick.js:98:9) requestId=7
2016-11-16T20:53:47.646Z - error: Error during processing request: http://IP_ADDRESS/api/report details: Error during rendering report: socket hang up Error: socket hang up
    at createHangUpError (_http_client.js:252:15)
    at Socket.socketOnEnd (_http_client.js:344:23)
    at emitNone (events.js:91:20)
    at Socket.emit (events.js:185:7)
    at endReadableNT (_stream_readable.js:974:12)
    at _combinedTickCallback (internal/process/next_tick.js:74:11)
    at process._tickCallback (internal/process/next_tick.js:98:9)
From previous event:
    at X:\Projects\jsreport\node_modules\jsreport-core\lib\render\render.js:144:54
    at _fulfilled (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:794:54)
    at self.promiseDispatch.done (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:823:30)
    at Promise.promise.promiseDispatch (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:756:13)
    at X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:564:44
    at flush (X:\Projects\jsreport\node_modules\listener-collection\node_modules\q\q.js:110:17)
    at _combinedTickCallback (internal/process/next_tick.js:67:7)
    at process._tickCallback (internal/process/next_tick.js:98:9)

wow! that is a large document, i haven't tested generating pdf with electron on that scale.
something i will need to investigate on my free time..

I am wanting to use "electron-server" as documentation says that it has most performance....

in fact, the most performant strategy is electron-ipc, seems like i need to update the docs...
electron-ipc was created because we had found some issues with electron-server on windows and electron-ipc solved all the problems.


A table documentation that does not document the table

This is a poor documentation. It doesn't explain the AccessRight column of the Privilege table.
Like what does it mean to have and AccessRight = 2 or an AccessRight = 65536?
It sure looks like a bit mask.
But shouldn't the bit mask meaning be explained in this page, which is the official table documentation???
Please document the tables properly in each table's documentation page.
Thanks!

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: e6505dc5-28a1-fc1a-7577-51f7adbcbb21
Version Independent ID: 7904c285-f1fb-6246-204f-716bd5ed8e02
Content: Privilege table/entity reference (Microsoft Dataverse) - Power Apps
Content Source: powerapps-docs/developer/data-platform/reference/entities/privilege.md
Service: powerapps
GitHub Login: @KumarVivek
Microsoft Alias: kvivek

You can have a look here: https://docs.microsoft.com/en-us/dynamics365/customer-engagement/web-api/accessrights?view=dynamics-ce-odata-9
Or maybe even here: https://docs.microsoft.com/en-us/powerapps/developer/data-platform/security-access-coding

Thanks @darshSWL.
@felipe-molina-cashme Hope you have found the information you were looking for. Closing the issue.


Image alignment syntax not properly rendered in preview

Problem: {align="right"} after image syntax is rendered as text

See https://experienceleague.adobe.com/en/docs/internal-test/test/markdown-extension-issues#align-right-rendering

@geverit4 - I still see this issue in the 2.1.3 release. The {align="right"} parameter still shows up in preview.

Confirmed that this is still a problem.  I did not have enough test cases.  Should be fixed shortly.

Fixed in version 2.1.4.

@geverit4 - Great. If you make 2.1.4 available to me, I'll test it. And then I think we'll be ready for updating to the store.

@geverit4 - Fixed in 2.1.4. Thanks!


cmd/telegraf: -configdirectory only includes files ending in .conf

This requirement doesn't seem to be documented anywhere except by digging through the code:
	if name[len(name)-5:] != ".conf" {
		continue
	}

https://github.com/influxdb/telegraf/blob/889c0a50a4aeaf4b343fbd7d2b8d9236cfc25a5b/config.go#L415

looks good, thanks!

closed by d505be1


feat(notif-perm-request): implement notification permission request

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Support animating the size difference between pages

I'm trying to animate the size of the ExpandablePageView when children have different height. Wrapping ExpandablePageView with a AnimatedSize doesn't work for some reason (it just jumps directly to the new height without animation). Is there a workaround for this?

Hi @xinran-finch, ExpandablePageView has animationDuration and animationCurve parameters, would they work for you? If not, please describe your problem more precisely, ideally with some code example included.


Create packagevariantset.yaml example

We're getting rid of the v1alpha1 and going to the v1alpha2 implementation of PackageVariantSet, which is quite a bit more functional.
Take a look at #6 instead, it has 3 v1alpha2 PackageVariantSets. Also see https://github.com/GoogleContainerTools/kpt/pull/3930, in particular this file: https://github.com/GoogleContainerTools/kpt/pull/3930/files#diff-87293e2004e9b295a0880c583f473bf0a9ad6ba67e38d95d909e09ef7f7c0bc0

/approve

/check-cla

/easycla


Placeholder Illustration

@tladesignz I've attached a placeholder image to use temporarily for the connecting view illustration.

As discussed in the weekly call an hour ago: I'll wait for an improved version or alternatively the decision to go without for now.


Refactor creating strand object per each start and stop function during codegen

Description:
$Subject

Referred in issue https://github.com/ballerina-platform/ballerina-lang/issues/24616


Use custom HTTP client

Use cross-fetch to perform requests to the Eth1 node. This gives us much more control over request than what ethers offer:

Support for abort signals
Better error handling. packages/lodestar/test/e2e/eth1/jsonRpcHttpClient.test.ts tests many error cases to produce useful messages
Batch request support
ethers is still used in Lodestar for parsing the DepositEvent log and computing its topic.

Most of the changes are due to a re-organization of the eth1 client classes:

IJsonRpcClient: formats and parses JSON RPC requests

JsonRpcHttpClient: Wrapper around cross-fetch
JsonRpcIpcClient: TBD

Eth1JsonRpcClient: Implements the Eth1 JSON RPC methods that are necessary. Uses IJsonRpcClient
Eth1Provider: Main consumable class which extends Eth1JsonRpcClient and interacts with the deposit contract

Fixes https://github.com/ChainSafe/lodestar/issues/1041
Fixes https://github.com/ChainSafe/lodestar/issues/1552 (or gives us the opportunity to fix it if necessary)

file conflict


[Adding Tool]:

What tool are you adding and to what category?
We need to add a tool that can be helpful for startup enthusiasts who want to have a better understanding of Startup concepts.
Add Tool Link
Add tool link- https://ajuniorvc.com/
Code of Conduct

[X] I agree to follow this project's Code of Conduct
[X] The tool is free for use

is this issue solved?


Setup and update commands

The goal is to simplify the usage as much as possible. Users should just download the binary and then run loda setup to configure their environment. LODA should automatically check for updates and inform the user. There should be a new command loda update to install the latest version.

Updates will be handled through documentation for now


WIP: When a GET route is defined, also define HEAD

This is in reference to https://github.com/luckyframework/lucky/issues/601
This PR will need to be added in conjunction with another on Lucky to actually support this.

What a cool idea!


Reorganised documentation and added RBAC section

@aledbf please review. thanks!

This change is

@bowei Updated the docs as requested. Thanks!

/lgtm

/lgtm

/approve

\o//


bias annealing weight updates

I could be wrong but it does not seem that you are annealing the bias with important sampling as suggested in the paper(3.4).
w_i = (1/N * 1/P(i))^beta
I think you would have to multiply this w_i term with your gradients

My apologies, I thought you had included prioritized replay.


[CNVS Upgrade] Introduce the collapsing panel component

This implements a very rudimentary collapsing panel, for use in the service create workflow.
I didn't invest any energy to make it match the mockups perfectly because apparently the mockups are not final. This just gives the developers something to work with.

Two small comments


Unable to resolve "@shopify/react-native-skia"

Im getting this error when a try to run the app with Expo in Android. I'm using the react-native-svg.
Android Bundling failed 5672ms
Unable to resolve "@shopify/react-native-skia" from<EMAIL_ADDRESS>

https://wuba.github.io/react-native-echarts/docs/advanced-guides/import-individually

When I changed:
import { SvgChart, SVGRenderer } from '@wuba/react-native-echarts'
Per:
 import SvgChart, { SVGRenderer } from '@wuba/react-native-echarts/svgChart'
I get the following error in Next.JS:

TypeError: root.appendChild is not a function

so you want to use in Android and next ?

If you want to use it in next.js, you need to add some configuration like this:
https://wuba.github.io/react-native-echarts/docs/advanced-guides/use-in-react#custom-react-projects

Can you verify this on version 1.2.2-alpha.4?

It looks like it worked on both (NextJS and Expo). Thank you very much! @zhiqingchen


File size limit not honored

This article says the maximum file size on a file column is 10GB, but I'm getting an error message trying to set it to 500MB.

This limitation is driving us away from using the file column in our module. Since this is urgent for us, could you confirm whether the max limit has changed on a file column, or we might have hit some glitch.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: a8f3598c-cd0e-2cd8-fa6b-5663f63c36bf
Version Independent ID: 656dca2a-e65d-fdb9-545e-b73d906ce4f1
Content: Column data types in Microsoft Dataverse (contains video) - Power Apps
Content Source: powerapps-docs/maker/data-platform/types-of-fields.md
Service: powerapps
Sub-service: dataverse-maker
GitHub Login: @Mattp123
Microsoft Alias: matp

Thanks for your comment, @tianhliu. This issue has been assigned to the doc author for review.

@tianhliu - Thank you for bringing this to my attention. I've confirmed that the max file size you can set is 131072 KB (131 MB) and correct in the UI when you attempt to exceed the limit. The article has been updated to reflect the correct maximum.
Closing issue.

Minor correction: 131072 KB = 128 MB


fix: GridForm Sweet Container - add missing label

Overview

The sweet container/honeypot was not applying the label, so accessibility was getting axed.  This fixes that.

PR Checklist

[ ] Related to designs:
[x] Related to JIRA ticket: GROW-2381
[ ] I have run this code to verify it works
[ ] This PR includes unit tests for the code change

oopsies, sorry I didn't catch this

No worries!  I need to test this out before merging but I think this will do the trick

📬Published Alpha Packages:@codecademy/gamut-kit@0.3.114-alpha.d0260d.0@codecademy/gamut-labs@18.1.5-alpha.d0260d.0@codecademy/gamut@33.1.1-alpha.d0260d.0<EMAIL_ADDRESS>
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📬Published Alpha Packages:@codecademy/gamut-kit@0.3.114-alpha.f0bac4.0@codecademy/gamut-labs@18.1.5-alpha.f0bac4.0@codecademy/gamut@33.1.1-alpha.f0bac4.0<EMAIL_ADDRESS>
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Revert changes made in pr #537 to change DataTransfer.data typehint.

According to this issue, a PR was made to change the type of DataTransfer.data to Any rather than str.
This PR makes the type str instead of Any.

@mdwcrft Does this PR have any impact on our internal codebase?

The schema specifies

I would suggest also that we add a comment, to explain this complication in the standard in detail - I might prevent it reverted again into the future?

@Jared-Newell-Mobility ready for review (once tests pass)

I've check over things again, I don't see a type specified in the schema. There is clearly a discrepancy between the descriptions 1. Optional. Data without specified length or format, in response to request and 2. text, data without specified length or format.
Schema normally trumps; I'm going to raise it with the OCA at minimum this might be cleared up also for others in the next Errata
  "$schema": "http://json-schema.org/draft-06/schema#",
  "$id": "urn:OCPP:Cp:2:2020:3:DataTransferRequest",
  "comment": "OCPP 2.0.1 FINAL",
  "definitions": {
    "CustomDataType": {
      "description": "This class does not get 'AdditionalProperties = false' in the schema generation, so it can be extended with arbitrary JSON properties to allow adding custom data.",
      "javaType": "CustomData",
      "type": "object",
      "properties": {
        "vendorId": {
          "type": "string",
          "maxLength": 255
        }
      },
      "required": [
        "vendorId"
      ]
    }
  },
  "type": "object",
  "additionalProperties": false,
  "properties": {
    "customData": {
      "$ref": "#/definitions/CustomDataType"
    },
    "messageId": {
      "description": "May be used to indicate a specific message or implementation.\r\n",
      "type": "string",
      "maxLength": 50
    },
-----------------
    "data": {
      "description": "Data without specified length or format. This needs to be decided by both parties (Open to implementation).\r\n"
    },
-----------------
    "vendorId": {
      "description": "This identifies the Vendor specific implementation\r\n\r\n",
      "type": "string",
      "maxLength": 255
    }
  },
  "required": [
    "vendorId"
  ]
}```

```{
  "$schema": "http://json-schema.org/draft-06/schema#",
  "$id": "urn:OCPP:Cp:2:2020:3:DataTransferResponse",
  "comment": "OCPP 2.0.1 FINAL",
  "definitions": {
    "CustomDataType": {
      "description": "This class does not get 'AdditionalProperties = false' in the schema generation, so it can be extended with arbitrary JSON properties to allow adding custom data.",
      "javaType": "CustomData",
      "type": "object",
      "properties": {
        "vendorId": {
          "type": "string",
          "maxLength": 255
        }
      },
      "required": [
        "vendorId"
      ]
    },
    "DataTransferStatusEnumType": {
      "description": "This indicates the success or failure of the data transfer.\r\n",
      "javaType": "DataTransferStatusEnum",
      "type": "string",
      "additionalProperties": false,
      "enum": [
        "Accepted",
        "Rejected",
        "UnknownMessageId",
        "UnknownVendorId"
      ]
    },
    "StatusInfoType": {
      "description": "Element providing more information about the status.\r\n",
      "javaType": "StatusInfo",
      "type": "object",
      "additionalProperties": false,
      "properties": {
        "customData": {
          "$ref": "#/definitions/CustomDataType"
        },
        "reasonCode": {
          "description": "A predefined code for the reason why the status is returned in this response. The string is case-insensitive.\r\n",
          "type": "string",
          "maxLength": 20
        },
        "additionalInfo": {
          "description": "Additional text to provide detailed information.\r\n",
          "type": "string",
          "maxLength": 512
        }
      },
      "required": [
        "reasonCode"
      ]
    }
  },
  "type": "object",
  "additionalProperties": false,
  "properties": {
    "customData": {
      "$ref": "#/definitions/CustomDataType"
    },
    "status": {
      "$ref": "#/definitions/DataTransferStatusEnumType"
    },
    "statusInfo": {
      "$ref": "#/definitions/StatusInfoType"
    },
-------------------------
    "data": {
      "description": "Data without specified length or format, in response to request.\r\n"
    }
-------------------------
  },
  "required": [
    "status"
  ]
}```

The OCA has confirmed that AnyType is any type as expected and is reflected in the schema. However, in OCPP 2.0.1 Part 2, Section 2.1.4. Primitive Datatypes the description for AnyType could be improved on, that is to remove the word "Text". This has been raised. So given this, I will close this PR as the current type used is correct.

Thanks for your effort @Jared-Newell-Mobility


Convert table of exclusions to the one needed for our workflow

This PR closes #115. Currently just working on converting from one format to the way our model expects it to be formatted which is just by location, forecast date, and date to exclude. Might need to just make this manually though

Closing this as there is no single table of exclusion history


feat: Handle Unicode domains in Plan and DomainFilter

Description
Ensure support for Internationalized Domain Names for Applications (aka. domains using Unicode) using golang.org/x/net/idna
Disclaimer, this is my first Go code so please look at it with extra skepticism
Checklist

[x] Unit tests updated
[] End user documentation updated

Pull Request Test Coverage Report for Build<PHONE_NUMBER>9
Details

10 of 12 (83.33%)  changed or added relevant lines in 2 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.005%) to 52.583%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

plan/plan.go
2
4
50.0%

Totals

Change from base Build<PHONE_NUMBER>2:
0.005%

Covered Lines:
14749

Relevant Lines:
28049

💛  - Coveralls


feat(simplex): Add cEUR support for simplex

Description

Tested
tested manually
How others should test
Can't easily be tested until a new cloud function is deployed, which must wait until after this is released. So no need to test.
Related issues

Enable Simplex cEUR

Hey @MuckT since "Can't easily be tested until a new cloud function is deployed, which must wait until after this is released. So no need to test." is mentioned in how others should test section we are skipping this test case
Cc: @jh2oman


#1716 should be reopened.

#1716 should be reopened
Some people still cannot use docker-machine with VB 5.0.10
See my comment or https://github.com/docker/machine/issues/1716#issuecomment-161369319

Please comment there, be sure to include --debug output and as much information as possible.  Thanks!

@nathanleclaire Would you reopen #1716  ?

@gotcha Mind opening a new issue with the symptoms that you're seeing?  Usually threads like that tend to be long-winded and digress from the original issue quite a bit.