ronlek / fastv2c-handnet Goto Github PK

I find that your model file size is 11.4M, why you say your model size is 42M?

I have a question about whether you should generate center__.txt in advance? How to generate it ? Thank you.

I find you provide "center_train_*_refined" file, but I don't know which files each line corresponds to. Could you give me some advice? Thank you very much.

Environment: Ubuntu RTX 2080ti 11g

When I try to train with MSRA dataset, I find that the training speed is very slow, and the batch size can only be set to 2. How can I increase the size of batch size? Why can a video card with 11g memory only read 2 batch sizes? How can I improve my training speed? How many epochs do you need to train? Thank you for response.

Hello!
I was trying to reproduce experiments with your solution. When I run train.py I have this error:

Traceback (most recent call last):
  File "train.py", line 145, in <module>
    net = model_inst(input_channels = 1, output_channels = keypoints_num) 
  File "/home/oriuser/mounted/src/mymodel.py", line 176, in model_inst
    x = Dense(44, activation = 'relu')(x) #Changed
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 591, in __call__
    self._maybe_build(inputs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py", line 1881, in _maybe_build
    self.build(input_shapes)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py", line 1005, in build
    raise ValueError('The last dimension of the inputs to `Dense` '
ValueError: The last dimension of the inputs to `Dense` should be defined. Found `None`.

I tried to fix that commenting out this line in mymodel.py

x = Reshape((output_channels, -1))(x)

And got an error:

Traceback (most recent call last):
  File "train.py", line 177, in <module>
    history = net.fit_generator(train_set, steps_per_epoch = steps_per_epoch_train, epochs = epochs_num, verbose = 1, callbacks = [cp_callback], validation_data = val_set, validation_steps = steps_per_epoch_val, workers = 0, use_multiprocessing = False, shuffle = True, initial_epoch = 0) #Changed
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1433, in fit_generator
    steps_name='steps_per_epoch')
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py", line 264, in model_iteration
    batch_outs = batch_function(*batch_data)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1153, in train_on_batch
    extract_tensors_from_dataset=True)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 2692, in _standardize_user_data
    y, self._feed_loss_fns, feed_output_shapes)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_utils.py", line 549, in check_loss_and_target_compatibility
    ' while using as loss `' + loss_name + '`. '
ValueError: A target array with shape (4, 21, 3) was passed for an output of shape (None, 21, 22, 22, 3) while using as loss `mean_squared_error`. This loss expects targets to have the same shape as the output.

Could you please specify tensorflow and keras versions? (I guess this is the issue: I use different versions of tensorflow)

If not, is there any suggestion to fix those errors?

I try to run "hand render" code, but it cannot get expected result.

Hi,

Thank you for open-sourcing this project.

How can I evaluate the model with awesome-hand-pose-estimation? It seems the test.py can only generate 2124 samples, while the awesome-hand-pose-estimation requires the whole MSRA dataset.

Is it possible to enable the whole dataset evaluation or individually?

Thanks

I do not find the "mean_error_all" in your paper, could you provide it? We can find the way to calculate "mean_error_all" in show_accuracy.py. Thank you very much.

In V2V github website "https://github.com/mks0601/V2V-PoseNet_RELEASE". I find the functions pixel2world and world2pixel are implemented differently for different datasets.
In dataset MSRA: world2pixel(x,y,z) local pixelY = imgHeight/2 - fy * torch.cdiv(y,z)
In dataset ICVL: world2pixel(x,y,z) local pixelY = imgHeight/2 + fy * torch.cdiv(y, z)
So can you provide your ICVL dataset related code? The formulate is whether right? Thank you!

@RonLek , nice work! Thanks for sharing!
Wonder if you have managed to improve the accuracy while maintaining the similar speed.

I am using the test.py script to generate the predictions so I can later evaluate the model using the https://github.com/xinghaochen/awesome-hand-pose-estimation framework.

For msra the total number of predictions in the generated txt file is 8496 while the expected number of predictions in the https://github.com/xinghaochen/awesome-hand-pose-estimation is 76375.

Therefore i am getting an error while trying to generate the scores :

ValueError: operands could not be broadcast together with shapes (76375,21,3) (8496,21,3)

Shouldn't the test.py script generate a bigger list of predictions for this dataset? Or am I doing something wrong?