Hi Pascal,
Input dimension is 5; 
problem fixed: I use pool_2d code 
thanks

On Tuesday, October 11, 2016 at 4:43:58 PM UTC+2, Pascal Lamblin wrote:
>
> Hi, 
>
> The code throwing the exception is: 
> > if x.type.ndim != 4: 
> >     raise TypeError() 
>
> What is the number of dimensions of 'input' in your case? 
>
> Usually, the `pool_2d` helper function takes care of reshaping the input 
> if necessary, and passing correctly ws/ds to the underlying Op. Is there 
> any reason in particular you are calling Pool directly? 
>
> Also, please note that if you edit your post in the web interface, it 
> sends a new message to the list each time. 
>
> On Tue, Oct 11, 2016, luca.wag...@gmail.com <javascript:> wrote: 
> > 
> > Hi Pascal, 
> > in maxpool3d.py 
> > I tried op = pool.Pool(ignore_border=False, mode='max', 
> openmp=None)(input, 
> > ws=(ds[1],ds[2])) 
> >  instead of op = pool.Pool((ds[1],ds[2]), ignore_border) that worked in 
> the 
> > previous Theano version. 
> > 
> > 
> > This is the output : 
> > 
> > Python 2.7.12 |Anaconda custom (64-bit)| (default, Jul  2 2016, 
> 17:42:40) 
> > [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 
> > Type "help", "copyright", "credits" or "license" for more information. 
> > Anaconda is brought to you by Continuum Analytics. 
> > Please check out: http://continuum.io/thanks and https://anaconda.org 
> > >>> runfile('/home/luca/data/ 
> > 
> DeepLearningTutorials/Theano-3D-ConvNet-master/convnet3d/core/run_multi_conv_t.py',
>  
>
> > 
> wdir='/home/luca/data/DeepLearningTutorials/Theano-3D-ConvNet-master/convnet3d/core')
>  
>
> > Using gpu device 0: GeForce 840M (CNMeM is disabled, cuDNN 5103) 
> > Traceback (most recent call last): 
> >   File "<stdin>", line 1, in <module> 
> >   File 
> > 
> "/home/luca/anaconda2/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py",
>  
>
> > line 714, in runfile 
> >     execfile(filename, namespace) 
> >   File 
> > 
> "/home/luca/anaconda2/lib/python2.7/site-packages/spyderlib/widgets/externalshell/sitecustomize.py",
>  
>
> > line 81, in execfile 
> >     builtins.execfile(filename, *where) 
> >   File 
> > 
> "/home/luca/data/DeepLearningTutorials/Theano-3D-ConvNet-master/convnet3d/core/run_multi_conv_t.py",
>  
>
> > line 32, in <module> 
> >     run_experiments() 
> >   File 
> > 
> "/home/luca/data/DeepLearningTutorials/Theano-3D-ConvNet-master/convnet3d/core/run_multi_conv_t.py",
>  
>
> > line 25, in run_experiments 
> >     Learning_rate=0.001 
> >   File "mpr_convnet_class_t.py", line 176, in __init__ 
> >     self.pool_layer_dim)) 
> >   File "convnet3d.py", line 255, in __init__ 
> >     out = max_pool_3d(input,pool_shape) 
> >   File "maxpool3d.py", line 53, in max_pool_3d 
> >     op = Pool(ignore_border=False, mode='max', openmp=None)(input, 
> > ws=(ds[1],ds[2])) 
> >   File "/home/luca/data/Theano-master/theano/gof/op.py", line 602, in 
> > __call__ 
> >     node = self.make_node(*inputs, **kwargs) 
> >   File "/home/luca/data/Theano-master/theano/tensor/signal/pool.py", 
> line 
> > 293, in make_node 
> >     raise TypeError() 
> > TypeError 
> > 
> > Many thanks 
> > Luca 
> > 
> > -- 
> > 
> > --- 
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>
> > """ Max pooling spatio-temporal inputs for Theano """ 
> > 
> > from theano import tensor 
> > from theano.tensor.signal.downsample import DownsampleFactorMax 
> > 
> > 
> > #it was originally ignore_border=False and then corrected as suggested 
> by Pascal 
> > '''Pascal update on ignore_border'''   
> > def max_pool_3d(input, ds, ignore_border=True): 
> >     """ 
> >     Takes as input a N-D tensor, where N >= 3. It downscales the input 
> video by 
> >     the specified factor, by keeping only the maximum value of 
> non-overlapping 
> >     patches of size (ds[0],ds[1],ds[2]) (time, height, width) 
> > 
> >     :type input: N-D theano tensor of input images. 
> >     :param input: input images. Max pooling will be done over the 3 last 
> dimensions. 
> >     :type ds: tuple of length 3 
> >     :param ds: factor by which to downscale. (2,2,2) will halve the 
> video in each dimension. 
> >     :param ignore_border: boolean value. When True, (5,5,5) input with 
> ds=(2,2,2) will generate a 
> >       (2,2,2) output. (3,3,3) otherwise. 
> >     """ 
> > 
> >     if input.ndim < 3: 
> >         raise NotImplementedError('max_pool_3d requires a dimension >= 
> 3') 
> > 
> >     # extract nr dimensions 
> >     vid_dim = input.ndim 
> >     # max pool in two different steps, so we can use the 2d 
> implementation of 
> >     # downsamplefactormax. First maxpool frames as usual. 
> >     # Then maxpool the time dimension. Shift the time dimension to the 
> third 
> >     # position, so rows and cols are in the back 
> > 
> >     # extract dimensions 
> >     frame_shape = input.shape[-2:] 
> >     
> >     # count the number of "leading" dimensions, store as dmatrix 
> >     # tensor.prod: product of every term in x along axis 
> >     batch_size = tensor.prod(input.shape[:-2]) 
> >     # Reshape x by right padding the shape with n_ones 1s. 
> >     batch_size = tensor.shape_padright(batch_size,1) 
> >     
> >     # store as 4D tensor with shape: (batch_size,1,height,width) 
> >     #tensor.cast     
> >     # Cast any tensor x to a Tensor of the same shape, but with a 
> different numerical type dtype. 
> >     new_shape = tensor.cast(tensor.join(0, batch_size, 
> >                                         tensor.as_tensor([1,]), 
> >                                         frame_shape), 'int32') 
> >     input_4D = tensor.reshape(input, new_shape, ndim=4) 
> > 
> >     # downsample mini-batch of videos in rows and cols 
> >     op = DownsampleFactorMax((ds[1],ds[2]), ignore_border) 
> >     
> >     output = op(input_4D) 
> >     # restore to original shape 
> >     outshape = tensor.join(0, input.shape[:-2], output.shape[-2:]) 
> >     out = tensor.reshape(output, outshape, ndim=input.ndim) 
> > 
> >     # now maxpool time 
> > 
> >     # output (time, rows, cols), reshape so that time is in the back 
> >     shufl = (list(range(vid_dim-3)) + 
> [vid_dim-2]+[vid_dim-1]+[vid_dim-3]) 
> >     input_time = out.dimshuffle(shufl) 
> >     # reset dimensions 
> >     vid_shape = input_time.shape[-2:] 
> >     
> >     # count the number of "leading" dimensions, store as dmatrix     
> >     batch_size = tensor.prod(input_time.shape[:-2]) 
> >     batch_size = tensor.shape_padright(batch_size,1) 
> >     
> >     # store as 4D tensor with shape: (batch_size,1,width,time) 
> >     new_shape = tensor.cast(tensor.join(0, batch_size, 
> >                                         tensor.as_tensor([1,]), 
> >                                         vid_shape), 'int32') 
> >     input_4D_time = tensor.reshape(input_time, new_shape, ndim=4) 
> >     # downsample mini-batch of videos in time 
> >     op = DownsampleFactorMax((1,ds[0]), ignore_border) 
> >     outtime = op(input_4D_time) 
> >     # output 
> >     # restore to original shape (xxx, rows, cols, time) 
> >     outshape = tensor.join(0, input_time.shape[:-2], outtime.shape[-2:]) 
> >     shufl = (list(range(vid_dim-3)) + 
> [vid_dim-1]+[vid_dim-3]+[vid_dim-2]) 
> >     return tensor.reshape(outtime, outshape, 
> ndim=input.ndim).dimshuffle(shufl) 
>
> -- 
> Pascal 
>

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