I'll try it. many thanks On Thursday, October 13, 2016 at 9:33:17 PM UTC+2, nouiz wrote: > > do you want 2d or 3d pooling? We merged (today I think) a good interface > for pool 3d: theano.tensor.signal.pool.pool_3d() > > that would be better then using the 2d pooling to mimic 3d pooling. > > On Wed, Oct 12, 2016 at 8:12 AM, <luca.wag...@gmail.com <javascript:>> > wrote: > >> 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 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 >>> > >>> > -- >>> > >>> > --- >>> > You received this message because you are subscribed to the Google >>> Groups "theano-users" group. >>> > To unsubscribe from this group and stop receiving emails from it, send >>> an email to theano-users...@googlegroups.com. >>> > For more options, visit https://groups.google.com/d/optout. >>> >>> > """ 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 >>> >> -- >> >> --- >> You received this message because you are subscribed to the Google Groups >> "theano-users" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to theano-users...@googlegroups.com <javascript:>. >> For more options, visit https://groups.google.com/d/optout. >> > >

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