yuanqing811/isic2018

ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection

isic2018's Issues

About the training CPU or GPU

Sorry, May I ask that this training process are using CPU or GPU? I don't know how to use GPU to train with this code.

Task3 issues

@yuanqing811 Thanks for your code!
There are some issues when running your code:
1- When running cls_train.py, it uses only 32 instances, and there are no instances for some catagories such as DF or VASC as in the screenshots. In the evaluation phase, the results are not as good as what you have stated. Here I have attached the results I get when running cls_eval.py
Could you please suggest the soulution to this issue?

![screenshot from 2018-07-05 10-27-50](https://user-images.githubusercontent.com/38858

584/42329213-280252e4-803e-11e8-82db-1e7b01c73f3d.png)

达到的最好成绩。

你好，请问一下你们使用新划分的数据集最高能达到多少？
我们去了10%的训练集作为验证集，最后验证集上的acc达到了90%左右，balanced_acc只有80%左右。

About script runs/cls_train.py

Hello, when i run the scripts runs/cls_train.py, i met the problems , can you help me solve it out? thanks very much!
Traceback (most recent call last):
File "", line 1, in
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\spawn.py", line 115, in _main
self = reduction.pickle.load(from_parent)
EOFError: Ran out of input
Exception in thread Thread-2:
Traceback (most recent call last):
File "D:\Anaconda3\envs\tensorflow_gpu\lib\threading.py", line 916, in _bootstrap_inner
self.run()
File "D:\Anaconda3\envs\tensorflow_gpu\lib\threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "D:\Anaconda3\envs\tensorflow_gpu\lib\site-packages\keras\utils\data_utils.py", line 548, in _run
with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
File "D:\Anaconda3\envs\tensorflow_gpu\lib\site-packages\keras\utils\data_utils.py", line 522, in
initargs=(seqs,))
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\context.py", line 119, in Pool
context=self.get_context())
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\pool.py", line 174, in init
self._repopulate_pool()
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\pool.py", line 239, in _repopulate_pool
w.start()
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\process.py", line 105, in start
self._popen = self._Popen(self)
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\popen_spawn_win32.py", line 65, in init
reduction.dump(process_obj, to_child)
File "D:\Anaconda3\envs\tensorflow_gpu\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
OverflowError: cannot serialize a bytes object larger than 4 GiB

ModuleNotFoundError: No module named 'datasets.ISIC2018'

I am trying to follow your instructions, but when I run

C:\Users\me\GitHub\ISIC2018Challenge>python runs/cls_train.py

I get

Traceback (most recent call last):
  File "runs/cls_train.py", line 4, in <module>
    from datasets.ISIC2018 import *
ModuleNotFoundError: No module named 'datasets.ISIC2018'

The folder /datasets/ISIC2018 subfolder is there, but apparently the import statement in cls_train.py misinterprets the folder as a module.

Did I miss a step in the setup?

ISIC2018_Task3_Training_LesionGroupings.csv not found

partition_task3_data tries to read a file

task3_sup_fname = 'ISIC2018_Task3_Training_LesionGroupings.csv' :

df = pd.read_csv(os.path.join(task3_gt_dir, task3_sup_fname))

Where can I find this file?

https://challenge.kitware.com/#phase/5abcbc6f56357d0139260e66

Only provides

ISIC2018_Task3_Training_GroundTruth.csv

Submission to task3 only achieves overall score 0.558

Hi, thanks for your code.

I trained the task 3 and followed the instruction. The result below is based on training a single model for 30 epochs, and is based on roughly 2000 validation images.

precision [0.54385965 0.88107324 0.64893617 0.51136364 0.67420814 0.4
0.725 ]
recall [0.4025974 0.91767372 0.68539326 0.67164179 0.62083333 0.22222222
0.85294118]

The results is closer to your reported results on validation images.

However, when I test with test images and submit the result with this model, the overall score is only 0.558. I Set TTA = False. The results is much lower than the reported results.
How can I solve this problem?

Many thanks for your reply.

dataset

Hello, could you upload the ISIC 2018 dataset? I downloaded from the web but always failed in about 50%.

About preprocess_data.py

Hello, I have 0.13.0 version of scikit_image, but anti_aliasing isn't available yet ,i got the error of "TypeError: resize() got an unexpected keyword argument 'anti_aliasing' , how could i solve this question? Thanks very much!

Could not find a version that satisfies the requirement keras-contrib==2.0.8

I tried to install requirement.txt using

pip install -r requirements.txt

But I got

Collecting Keras==2.1.6 (from -r requirements.txt (line 26))
  Using cached https://files.pythonhosted.org/packages/54/e8/eaff7a09349ae9bd40d3ebaf028b49f5e2392c771f294910f75bb608b241/Keras-2.1.6-py2.py3-none-any.whl
Collecting keras-contrib==2.0.8 (from -r requirements.txt (line 27))
  Could not find a version that satisfies the requirement keras-contrib==2.0.8 (from -r requirements.txt (line 27)) (from versions: )
No matching distribution found for keras-contrib==2.0.8 (from -r requirements.txt (line 27))

TypeError: can't pickle _thread.lock objects

Running cls_train.py with debug_visualize = True triggered the following error:

Epoch 1/50
Exception in thread Thread-2:
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\threading.py", line 916, in _bootstrap_inner
    self.run()
  File "C:\ProgramData\Anaconda3\lib\threading.py", line 864, in run
    self._target(*self._args, **self._kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\data_utils.py", line 548, in _run
    with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor:
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\data_utils.py", line 522, in <lambda>
    initargs=(seqs,))
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 119, in Pool
    context=self.get_context())
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py", line 174, in __init__
    self._repopulate_pool()
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py", line 239, in _repopulate_pool
    w.start()
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
TypeError: can't pickle _thread.lock objects

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 105, in spawn_main
    exitcode = _main(fd)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 115, in _main
    self = reduction.pickle.load(from_parent)
EOFError: Ran out of input

There may be a problem with

def save_model_config(run_name, **model_config):
    config_file = get_model_config_filename(run_name=run_name)
    with open(config_file, 'wb') as file:
        pickle.dump(model_config, file)

Apparently, using pickle to save Keras models is not recommended.

任务三分类

你们在任务三分类，除此之外还有哪些尝试和改进？谢谢。

parent class Backbone

请问是从哪继承的这个父类？

Have you published a paper?

Your code is really great! I wonder if you have published a paper?

KeyError: 0 in keras\utils\data_utils.py

Running

cls_train.py

on a Windows 10 machine

triggers a KeyError in keras\utils\data_utils.py

with the following output log:

C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
10015it [00:00, 22019.01it/s]
2018-06-17 18:26:57.254087: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to  
==================================================================================================
input_1 (InputLayer)            (None, 224, 224, 3)  0                         
....
predictions (Dense)             (None, 7)            903         dropout_1[0][0]
__________________________________________________________________________________________________
outputs (Activation)            (None, 7)            0           predictions[0][0]
==================================================================================================
Total params: 22,065,959
Trainable params: 22,031,527
Non-trainable params: 34,432
__________________________________________________________________________________________________
num_dense_layers     : 1
num_dense_units      : 128
dropout_rate         : 0.0
pooling              : avg
class_wt_type        : ones
dense_layer_regularizer : L1
class_wt_type        : ones
learning_rate        : 0.0001
batch_size           : 32
use_data_aug         : True
horizontal_flip      : True
vertical_flip        : True
width_shift_range    : 0.1
height_shift_range   : 0.1
rotation_angle       : 180
n_samples_train      : 8012
n_samples_valid      : 2003
Epoch 1/50
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Using TensorFlow backend.
Using TensorFlow backend.
Using TensorFlow backend.
Using TensorFlow backend.
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Using TensorFlow backend.
Using TensorFlow backend.
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\data_utils.py", line 390, in get_index
    return _SHARED_SEQUENCES[uid][i]
KeyError: 0
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\data_utils.py", line 564, in get
    inputs = self.queue.get(block=True).get()
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\pool.py", line 644, in get
    raise self._value
KeyError: 0

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "C:\Users\Ralph\Documents\GitHub\ISIC2018Challenge\runs\cls_train.py", line 131, in <module>
    use_multiprocessing=True)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 2212, in fit_generator
    generator_output = next(output_generator)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\data_utils.py", line 570, in get
    six.raise_from(StopIteration(e), e)
  File "<string>", line 3, in raise_from
StopIteration: 0

Change request: Provide an evaluation setup for development

For development, it would be really convenient to have a setup running on only a few samples, to get a quicker feedback. I have already tried and removed the majority of the images and the corresponding rows in the csv for Task 3. But then I ran into

_init__.py", line 232, in partition_task3_data
    y_valid = y[valid_indices]
IndexError: boolean index did not match indexed array along dimension 0; dimension is 10015 but corresponding boolean dimension is 59

IndexError: index 0 is out of bounds for axis 0 with size 0

Hi,
I want to ask about this error I encountered when trying to run seg_train.py:
Traceback (most recent call last):
File "seg_train.py", line 107, in
if y_train[0].max() > 1:
IndexError: index 0 is out of bounds for axis 0 with size 0

I cannot proceed to train after loading the image.
Can you help me with this?
Thank you and best regards,
Duc