It's my first time using it.
I study machine learning using a chain called Convolutional LSTM.
In an experiment using MovingMnist, the program worked, but when I tried to use the data I had, I got an error and I am in trouble.
The input data will be 320x256 bmp data (one channel).
Error Contents
/home/denko/anaconda3/lib/python3.6/site-packages/h5py/_init__.py:36: FutureWarning: Conversion of the second argument of issudtype from `float` to `np.floating` is decremented.Infuture, will be flat.naped type(64)
from._conv import register_converters as_register_converters
Exception in main training loop: Unsupported dtype object
Traceback (most recent call last):
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/training/trainer.py", line 299, in run
update()
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/training/updater.py", line 223, in update
self.update_core()
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/training/updater.py", line 228, in update_core
in_arrays=self.converter(batch,self.device)
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/dataset/convert.py", line 93, in concat_examples
[example[i] for example in batch], padding[i])))
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/dataset/convert.py", line 35, into_device
return cuda.to_gpu(x,device)
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/backends/cuda.py", line 275, into_gpu
return_array_to_gpu(array,device_,stream)
File"/home/denko/anaconda3/lib/python3.6/site-packages/chainer/backends/cuda.py", line322, in_array_to_gpu
return cupy.asarray(array)
File"/home/denko/anaconda3/lib/python 3.6/site-packages/cupy/creation/from_data.py", line61, in asarray
return core.array(a,dtype,false)
File "cupy/core/core.pyx", line 2070, incupy.core.core.array
File "cupy/core/core.pyx", line 2101, incupy.core.core.array
Will finalize trainer extensions and updater before re-raising the exception.
Traceback (most recent call last):
File "train.py", line 81, in<module>
train()
File "train.py", line 69, intrain
trainer.run()
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/training/trainer.py", line 313, in run
Six.release (*sys.exc_info())
File"/home/denko/anaconda3/lib/python 3.6/site-packages/six.py", line 693, inrelease
raise value
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/training/trainer.py", line 299, in run
update()
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/training/updater.py", line 223, in update
self.update_core()
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/training/updater.py", line 228, in update_core
in_arrays=self.converter(batch,self.device)
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/dataset/convert.py", line 93, in concat_examples
[example[i] for example in batch], padding[i])))
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/dataset/convert.py", line 35, into_device
return cuda.to_gpu(x,device)
File"/home/denko/anaconda3/lib/python 3.6/site-packages/chainer/backends/cuda.py", line 275, into_gpu
return_array_to_gpu(array,device_,stream)
File"/home/denko/anaconda3/lib/python3.6/site-packages/chainer/backends/cuda.py", line322, in_array_to_gpu
return cupy.asarray(array)
File"/home/denko/anaconda3/lib/python 3.6/site-packages/cupy/creation/from_data.py", line61, in asarray
return core.array(a,dtype,false)
File "cupy/core/core.pyx", line 2070, incupy.core.core.array
File "cupy/core/core.pyx", line 2101, incupy.core.core.array
ValueError: Unsupported dtype object
code
--dataset_for_rr --
import numpy as np
import chain
from PIL import Image
from chain import cuda, Variable
classrrDataset(chain.dataset.DatasetMixin):
def_init__(self, l, r, inf, outf):
self.rr_list=np.load('/media/denko/OS/data/rr_list/test.npy')
self.l=l
self.r = r
self.inf = inf
self.outf = outf
self.frames = self.inf + self.outf
self.num = self.r-self.l
self.xp=cuda.cupy
def__len__(self):
return self.num
def get_example(self, i):
self.ind=self.l+i
self.data=np.empty(self.frames, 1,256,320), dtype=np.float32)
for jin range (self.frames):
self.bmp_name = str(int(self.rr_list[self.ind][j]))
self.year=self.bmp_name [0:4]
self.month=self.bmp_name [4:6]
self.path='/media/denko/OS/data/bmp/'+self.year+'/'+self.month+'/'+self.bmp_name+'.bmp'
self.bmp_data=Image.open(self.path)
self.bmp_data=np.array(self.bmp_data).astype(dtype=np.float32)
self.bmp_data =self.bmp_data/255
self.data [j, 0, :, :] = self.bmp_data
self.data=Variable(np.array(self.data,dtype=np.float32))
return self.data [:self.inf, 0, :, :], self.data [self.inf:self.frames, 0, :, :]
In short,
34self.data=Variable(np.array(self.data,dtype=np.float32))
34self.data=np.array(self.data,dtype=np.float32)
It would be all right if
The details are as follows.
It was hard to understand, so I added a line.
1 import numpy as np
2 import chain
3 from PIL import Image
4 from chain import cuda, Variable
5
6 class rrDataset(chain.dataset.DatasetMixin):
7def_init__(self, l, r, inf, outf):
8self.rr_list=np.load('/media/denko/OS/data/rr_list/test.npy')
9self.l=l
10self.r=r
11self.inf = inf
12self.outf=outf
13 self.frames = self.inf + self.outf
14 self.num = self.r-self.l
15self.xp=cuda.cupy
16
17 def__len__(self):
18 return self.num
19
20def get_example(self, i):
21 self.ind=self.l+i
22 self.data=np.empty(self.frames, 1,256,320), dtype=np.float32)
23
24 for jin range (self.frames):
25self.bmp_name = str(int(self.rr_list[self.ind][j]))
26 self.year=self.bmp_name [0:4]
27 self.month=self.bmp_name [4:6]
28self.path='/media/denko/OS/data/bmp/'+self.year+'/'+self.month+'/'+self.bmp_name+'.bmp'
29self.bmp_data=Image.open(self.path)
30self.bmp_data=np.array(self.bmp_data).astype(dtype=np.float32)
31self.bmp_data =self.bmp_data/255
32 self.data [j, 0, :, :] = self.bmp_data
33
34 self.data=Variable(np.array(self.data,dtype=np.float32))
35
36 return self.data [:self.inf, 0, :, :], self.data [:self.inf:self.frames, 0, :, :]
The problem is that, as Metropolis points out, we are returning Variable in line 34. DatasetMixin is usually expected to return np.ndarray or np.ndarray tuples.
Here is a short code for verification.
1 from chain.dataset.convert import concat_examples
2 from chain import Variable
3 import numpy as np
4
5 data_np_1 = np.range (9, dtype=np.float32).reshape ((3,3))
6 data_np_2 = data_np_1*2
7 data_Variable_1 = Variable(data_np_1)
8 data_Variable_2 = Variable(data_np_2)
9
10batch_1_np=(data_np_1[:1,:], data_np_1[1:,:])
11batch_2_np=(data_np_2[:1,:], data_np_2[1:,:])
12
13batch_1_Variable=(data_Variable_1[:1,:], data_Variable_1[1:,:])
14batch_2_Variable=(data_Variable_2[:1,:], data_Variable_2[1:,:])
15
16 print(concat_examples([batch_1_np,batch_2_np]))
17 print(concat_examples([batch_1_Variable, batch_2_Variable]))
18
The results are as follows.
(
array([
[[0., 1., 2.]],
[[0., 2., 4.]]
, dtype=float32),
array([
[[ 3., 4., 5.], [ 6., 7., 8.]],
[[ 6., 8., 10.], [12., 14., 16.]]
, dtype=float32)
)
(
array([
[[variable(0.), variable(1.), variable(2.)],
[[variable(0.), variable(2.), variable(4.)]]
, dtype=object),
array([
[[variable(3.), variable(4.), variable(5.), variable(6.), variable(7.), variable(8.)],
[[variable(6.), variable(8.), variable(10.), variable(12.), variable(14.), variable(16.)]]]
, dtype = object )
)
In this way, ndarray with dtype of object is generated, so
ValueError: Unsupported dtype object
That's what they say.
In chain.dataset.convert._concat_arrays,
if not instance(arrays[0], numpy.ndarray) and \
not isinstance(arrays[0], cuda.ndarray):
arrays=numpy.asarray(arrays)
is called, and if you give variable to numpy.asarray, the return value will not be the normal ndarray.
Below are Tips, so you can skip them.
inf is a reserved word for Infinity in math. It is recommended not to use it.
Similarly, the .data attribute is a reservation word for pointer to memory with numpy and cupy, and xp.ndarray with Chainer, so it is recommended not to use it yourself. (Actually, the reservation word has been removed from v3.0.0 or later, because it avoids conflict with numpy and cupy.)
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