def maxpool_3D(input, ds, ignore_border=False): #input.dimshuffle (0, 2, 1, 3, 4) # convert to make video in back. # no need to reshuffle. 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 batch_size = T.prod(input.shape[:-2]) batch_size = T.shape_padright(batch_size,1) # store as 4D tensor with shape: (batch_size,1,height,width) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([1,]), frame_shape), 'int32') input_4D = T.reshape(input, new_shape, ndim=4) # downsample mini-batch of videos in rows and cols op = DownsampleFactorMax((ds[1],ds[2]), ignore_border) # so second and third dimensions of ds are for height and width output = op(input_4D) # restore to original shape outshape = T.join(0, input.shape[:-2], output.shape[-2:]) out = T.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 = T.prod(input_time.shape[:-2]) batch_size = T.shape_padright(batch_size,1) # store as 4D tensor with shape: (batch_size,1,width,time) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([1,]), vid_shape), 'int32') input_4D_time = T.reshape(input_time, new_shape, ndim=4) # downsample mini-batch of videos in time op = DownsampleFactorMax((1,ds[0]), ignore_border) # Here the time dimension is downsampled. outtime = op(input_4D_time) # output # restore to original shape (xxx, rows, cols, time) outshape = T.join(0, input_time.shape[:-2], outtime.shape[-2:]) shufl = (list(range(vid_dim-3)) + [vid_dim-1]+[vid_dim-3]+[vid_dim-2]) #rval = T.reshape(outtime, outshape, ndim=input.ndim).dimshuffle(shufl) return T.reshape(outtime, outshape, ndim=input.ndim).dimshuffle(shufl)
def max_pool_3d(input, ds, ignore_border=False): """ 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. Example 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') vid_dim = input.ndim #Maxpool frame frame_shape = input.shape[-2:] # count the number of "leading" dimensions, store as dmatrix batch_size = T.prod(input.shape[:-2]) batch_size = T.shape_padright(batch_size, 1) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([ 1, ]), frame_shape), 'int32') input_4D = T.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 = T.join(0, input.shape[:-2], output.shape[-2:]) out = T.reshape(output, outshape, ndim=input.ndim) #Maxpool time # output (time, rows, cols), reshape so that time is in the back shufl = (list(range(vid_dim - 4)) + list(range(vid_dim - 3, vid_dim)) + [vid_dim - 4]) input_time = out.dimshuffle(shufl) # reset dimensions vid_shape = input_time.shape[-2:] # count the number of "leading" dimensions, store as dmatrix batch_size = T.prod(input_time.shape[:-2]) batch_size = T.shape_padright(batch_size, 1) # store as 4D tensor with shape: (batch_size,1,width,time) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([ 1, ]), vid_shape), 'int32') input_4D_time = T.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) # restore to original shape (xxx, rows, cols, time) outshape = T.join(0, input_time.shape[:-2], outtime.shape[-2:]) shufl = (list(range(vid_dim - 4)) + [vid_dim - 1] + list(range(vid_dim - 4, vid_dim - 1))) #shufl = (list(range(vid_dim-3)) + [vid_dim-1]+[vid_dim-3]+[vid_dim-2]) return T.reshape(outtime, outshape, ndim=input.ndim).dimshuffle(shufl)
def test_infer_shape(self): image = tensor.dtensor4() maxout = tensor.dtensor4() gz = tensor.dtensor4() rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2)) image_val = rng.rand(4, 6, 7, 9) out_shapes = [[[[4, 6, 7, 9], [4, 6, 7, 9]], [[4, 6, 3, 4], [4, 6, 4, 5]], [[4, 6, 2, 3], [4, 6, 3, 3]], [[4, 6, 3, 3], [4, 6, 4, 3]], [[4, 6, 2, 4], [4, 6, 3, 5]]], [[None, None], [[4, 6, 4, 5], None], [[4, 6, 3, 3], None], [[4, 6, 4, 3], None], [[4, 6, 3, 5], None]], [[None, None], [None, None], [[4, 6, 3, 4], None], [[4, 6, 4, 4], None], [None, None]]] for i, maxpoolshp in enumerate(maxpoolshps): for j, ignore_border in enumerate([True, False]): for k, padding in enumerate([(0, 0), (1, 1), (1, 2)]): if out_shapes[k][i][j] is None: continue # checking shapes generated by DownsampleFactorMax self._compile_and_check([image], [DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, padding=padding)(image)], [image_val], DownsampleFactorMax) # checking shapes generated by MaxPoolGrad maxout_val = rng.rand(*out_shapes[k][i][j]) gz_val = rng.rand(*out_shapes[k][i][j]) self._compile_and_check([image, maxout, gz], [MaxPoolGrad(maxpoolshp, ignore_border=ignore_border, padding=padding) (image, maxout, gz)], [image_val, maxout_val, gz_val], MaxPoolGrad, warn=False) # checking with broadcastable input image = tensor.tensor(dtype='float64', broadcastable=(False, False, True, True)) image_val = rng.rand(4, 6, 1, 1) self._compile_and_check( [image], [DownsampleFactorMax((2, 2), ignore_border=True, padding=(0, 0))(image)], [image_val], DownsampleFactorMax)
def max_pool_3d(input, ds, ignore_border=False): """ Perfrom 3D max-pooling :type input: theano.tensor :param input: input feature volumes :type ds: tuple of length 3 :param ds: factor by which to downscale, typically set as (2,2,2) :param ignore_border: boolean value. Example when True, (7,7,7) input with ds=(2,2,2) will generate a (3,3,3) output. (4,4,4) otherwise. """ vid_dim = input.ndim #Maxpool frame frame_shape = input.shape[-2:] # count the number of "leading" dimensions, store as dmatrix batch_size = T.prod(input.shape[:-2]) batch_size = T.shape_padright(batch_size, 1) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([ 1, ]), frame_shape), 'int32') input_4D = T.reshape(input, new_shape, ndim=4) op = DownsampleFactorMax((ds[1], ds[2]), ignore_border) output = op(input_4D) # restore to original shape outshape = T.join(0, input.shape[:-2], output.shape[-2:]) out = T.reshape(output, outshape, ndim=input.ndim) #Maxpool time # output (time, rows, cols), reshape so that time is in the back shufl = (list(range(vid_dim - 4)) + list(range(vid_dim - 3, vid_dim)) + [vid_dim - 4]) input_time = out.dimshuffle(shufl) # reset dimensions vid_shape = input_time.shape[-2:] # count the number of "leading" dimensions, store as dmatrix batch_size = T.prod(input_time.shape[:-2]) batch_size = T.shape_padright(batch_size, 1) # store as 4D tensor with shape: (batch_size,1,width,time) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([ 1, ]), vid_shape), 'int32') input_4D_time = T.reshape(input_time, new_shape, ndim=4) op = DownsampleFactorMax((1, ds[0]), ignore_border) outtime = op(input_4D_time) # restore to original shape (xxx, rows, cols, time) outshape = T.join(0, input_time.shape[:-2], outtime.shape[-2:]) shufl = (list(range(vid_dim - 4)) + [vid_dim - 1] + list(range(vid_dim - 4, vid_dim - 1))) return T.reshape(outtime, outshape, ndim=input.ndim).dimshuffle(shufl)
def test_DownsampleFactorMaxStride(self): rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((1, 1), (3, 3), (5, 3)) stridesizes = ((1, 1), (3, 3), (5, 7)) # generate random images imval = rng.rand(4, 10, 16, 16) outputshps = ((4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3), (4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3), (4, 10, 14, 14), (4, 10, 5, 5), (4, 10, 3, 2), (4, 10, 14, 14), (4, 10, 6, 6), (4, 10, 4, 3), (4, 10, 12, 14), (4, 10, 4, 5), (4, 10, 3, 2), (4, 10, 12, 14), (4, 10, 5, 6), (4, 10, 4, 3)) images = tensor.dtensor4() indx = 0 for maxpoolshp in maxpoolshps: for ignore_border in [True, False]: for stride in stridesizes: outputshp = outputshps[indx] indx += 1 #DownsampleFactorMax op numpy_output_val = \ self.numpy_max_pool_2d_stride(imval, maxpoolshp, ignore_border, stride) assert numpy_output_val.shape == outputshp, ( "outshape is %s, calculated shape is %s" % (outputshp, numpy_output_val.shape)) maxpool_op = \ DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, st=stride)(images) f = function([images], maxpool_op) output_val = f(imval) utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMaxPaddingStride_grad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) imgsizes = ((10, 10), (10, 5), (5, 5)) maxpoolsizes = ((5, 3), (3, 5), (3, 3)) stridesizes = ((3, 2), (2, 3), (3, 3)) paddingsizes = ((2, 2), (2, 1), (2, 2)) for i in range(len(imgsizes)): imgsize = imgsizes[i] imval = rng.rand(1, 1, imgsize[0], imgsize[1]) * 10.0 maxpoolsize = maxpoolsizes[i] stridesize = stridesizes[i] paddingsize = paddingsizes[i] grad_shape = DownsampleFactorMax.out_shape(imval.shape, maxpoolsize, st=stridesize, ignore_border=True, padding=paddingsize) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): out = DownsampleFactorMax( maxpoolsize, ignore_border=True, st=stridesize, padding=paddingsize, )(input) grad_op = MaxPoolGrad(maxpoolsize, ignore_border=True, st=stridesize, padding=paddingsize) return grad_op(input, out, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolPaddingStride_grad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) imgsizes = ((10, 10), (10, 5), (5, 5)) avgpoolsizes = ((5, 3), (3, 5), (3, 3)) stridesizes = ((3, 2), (2, 3), (3, 3)) paddingsizes = ((2, 2), (2, 1), (2, 2)) for i in range(len(imgsizes)): imgsize = imgsizes[i] imval = rng.rand(1, 1, imgsize[0], imgsize[1]) * 10.0 avgpoolsize = avgpoolsizes[i] stridesize = stridesizes[i] paddingsize = paddingsizes[i] # 'average_exc_pad' with non-zero padding is not implemented for mode in ['sum', 'average_inc_pad']: grad_shape = DownsampleFactorMax.out_shape(imval.shape, avgpoolsize, st=stridesize, ignore_border=True, padding=paddingsize) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): grad_op = AveragePoolGrad(avgpoolsize, ignore_border=True, st=stridesize, padding=paddingsize, mode=mode) return grad_op(input, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad_st_extra(self): """checks the gradient of the gradient for the case that stride is used for extra examples""" rng = numpy.random.RandomState(utt.fetch_seed()) avgpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9)) stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1), (2, 3), (10, 10), (1, 1)) imvsizs = ((16, 16), (16, 16), (16, 16), (8, 5), (8, 5), (8, 5), (8, 5)) for indx in numpy.arange(len(avgpoolshps)): imvsize = imvsizs[indx] imval = rng.rand(1, 2, imvsize[0], imvsize[1]) stride = stridesizes[indx] avgpoolshp = avgpoolshps[indx] for ignore_border in [True, False]: for mode in ['sum', 'average_inc_pad', 'average_exc_pad']: grad_shape = DownsampleFactorMax.out_shape( imval.shape, avgpoolshp, ignore_border=ignore_border, st=stride) grad_val = rng.rand(*grad_shape) def mp(input, grad): grad_op = AveragePoolGrad(avgpoolshp, ignore_border=ignore_border, st=stride, mode=mode) return grad_op(input, grad) # skip the grad verification when the output is empty if numpy.prod(grad_shape) == 0: continue utt.verify_grad(mp, [imval, grad_val], rng=rng)
def mp(input): return DownsampleFactorMax( maxpoolsize, ignore_border=True, st=stridesize, padding=paddingsize, )(input)
def test_infer_shape(self): image = tensor.dtensor4() maxout = tensor.dtensor4() gz = tensor.dtensor4() rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2)) image_val = rng.rand(4, 6, 7, 9) out_shapes = [[[4, 6, 7, 9], [4, 6, 7, 9]], [[4, 6, 3, 4], [4, 6, 4, 5]], [[4, 6, 2, 3], [4, 6, 3, 3]], [[4, 6, 3, 3], [4, 6, 4, 3]], [[4, 6, 2, 4], [4, 6, 3, 5]]] for i, maxpoolshp in enumerate(maxpoolshps): for j, ignore_border in enumerate([True, False]): # checking shapes generated by DownsampleFactorMax self._compile_and_check([image], [ DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border)(image) ], [image_val], DownsampleFactorMax) # checking shapes generated by DownsampleFactorMaxGrad maxout_val = rng.rand(*out_shapes[i][j]) gz_val = rng.rand(*out_shapes[i][j]) self._compile_and_check([image, maxout, gz], [ DownsampleFactorMaxGrad(maxpoolshp, ignore_border=ignore_border)( image, maxout, gz) ], [image_val, maxout_val, gz_val], DownsampleFactorMaxGrad, warn=False)
def test_DownsampleFactorMaxGrad_grad_st_extra(self): """checks the gradient of the gradient for the case that stride is used for extra examples""" rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9)) stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1), (2, 3), (10, 10), (1, 1)) imvsizs = ((16, 16), (16, 16), (16, 16), (8, 5), (8, 5), (8, 5), (8, 5)) for indx in numpy.arange(len(maxpoolshps)): imvsize = imvsizs[indx] imval = rng.rand(1, 2, imvsize[0], imvsize[1]) stride = stridesizes[indx] maxpoolshp = maxpoolshps[indx] for ignore_border in [True, False]: grad_shape = DownsampleFactorMax.out_shape( imval.shape, maxpoolshp, ignore_border=ignore_border, st=stride ) grad_val = rng.rand(*grad_shape) def mp(input, grad): out = DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, st=stride)(input) grad_op = DownsampleFactorMaxGrad(maxpoolshp, ignore_border=ignore_border, st=stride) return grad_op(input, out, grad) # skip the grad verification when the output is empty if numpy.prod(grad_shape) == 0: continue utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxPaddingStride(self): ignore_border = True # padding does not support ignore_border=False rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolsizes = [(3, 3), (4, 4), (3, 4), (4, 3), (2, 2)] stridesizes = [(2, 2), (2, 2), (1, 1), (1, 2), (2, 2)] paddingsizes = [(2, 2), (1, 2), (2, 1), (0, 0), (1, 1)] imgsizes = [(5, 5), (5, 5), (5, 6), (6, 5), (5, 5)] m = 4 # minibatch c = 2 # channel size images = tensor.dtensor4() for indx, mode in product( numpy.arange(len(maxpoolsizes)), ['max', 'sum', 'average_inc_pad', 'average_exc_pad']): imgsize = imgsizes[indx] imval = rng.rand(m, c, imgsize[0], imgsize[1]) - 0.5 stridesize = stridesizes[indx] maxpoolsize = maxpoolsizes[indx] paddingsize = paddingsizes[indx] numpy_output_val = self.numpy_max_pool_2d_stride_padding( imval, maxpoolsize, ignore_border, stridesize, paddingsize, mode) maxpool_op = DownsampleFactorMax(maxpoolsize, ignore_border=ignore_border, st=stridesize, padding=paddingsize, mode=mode)(images) f = function([images], maxpool_op) output_val = f(imval) utt.assert_allclose(output_val, numpy_output_val)
def test_AveragePoolPaddingStride_grad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) imgsizes = ((10, 10), (10, 5), (5, 5)) avgpoolsizes = ((5, 3), (3, 5), (3, 3)) stridesizes = ((3, 2), (2, 3), (3, 3)) paddingsizes = ((2, 2), (2, 1), (2, 2)) for i in range(len(imgsizes)): imgsize = imgsizes[i] imval = rng.rand(1, 1, imgsize[0], imgsize[1]) * 10.0 avgpoolsize = avgpoolsizes[i] stridesize = stridesizes[i] paddingsize = paddingsizes[i] # 'average_exc_pad' with non-zero padding is not implemented for mode in ['sum', 'average_inc_pad']: grad_shape = DownsampleFactorMax.out_shape(imval.shape, avgpoolsize, st=stridesize, ignore_border=True, padding=paddingsize) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): grad_op = AveragePoolGrad(avgpoolsize, ignore_border=True, st=stridesize, padding=paddingsize, mode=mode) return grad_op(input, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxGrad_grad_st(self): """checks the gradient of the gradient for the case that stride is used""" rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((1, 1), (3, 3), (5, 3)) stridesizes = ((1, 1), (3, 3), (5, 7)) imval = rng.rand(1, 2, 16, 16) for maxpoolshp in maxpoolshps: for ignore_border in [True, False]: for stride in stridesizes: grad_shape = DownsampleFactorMax.out_shape( imval.shape, maxpoolshp, ignore_border=ignore_border, st=stride) grad_val = rng.rand(*grad_shape) def mp(input, grad): out = DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, st=stride)(input) grad_op = MaxPoolGrad(maxpoolshp, ignore_border=ignore_border, st=stride) return grad_op(input, out, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxPaddingStride_grad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) imgsizes = ((10, 10), (10, 5), (5, 5)) maxpoolsizes = ((5, 3), (3, 5), (3, 3)) stridesizes = ((3, 2), (2, 3), (3, 3)) paddingsizes = ((2, 2), (2, 1), (2, 2)) for i in range(len(imgsizes)): imgsize = imgsizes[i] imval = rng.rand(1, 1, imgsize[0], imgsize[1]) * 10.0 maxpoolsize = maxpoolsizes[i] stridesize = stridesizes[i] paddingsize = paddingsizes[i] grad_shape = DownsampleFactorMax.out_shape(imval.shape, maxpoolsize, st=stridesize, ignore_border=True, padding=paddingsize) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): out = DownsampleFactorMax( maxpoolsize, ignore_border=True, st=stridesize, padding=paddingsize, )(input) grad_op = MaxPoolGrad(maxpoolsize, ignore_border=True, st=stridesize, padding=paddingsize) return grad_op(input, out, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad_st(self): """checks the gradient of the gradient for the case that stride is used""" rng = numpy.random.RandomState(utt.fetch_seed()) avgpoolshps = ((1, 1), (3, 3), (5, 3)) stridesizes = ((1, 1), (3, 3), (5, 7)) imval = rng.rand(1, 2, 16, 16) for avgpoolshp in avgpoolshps: for ignore_border in [True, False]: for mode in ['sum', 'average_inc_pad', 'average_exc_pad']: for stride in stridesizes: grad_shape = DownsampleFactorMax.out_shape( imval.shape, avgpoolshp, ignore_border=ignore_border, st=stride) grad_val = rng.rand(*grad_shape) def mp(input, grad): grad_op = AveragePoolGrad( avgpoolshp, ignore_border=ignore_border, st=stride, mode=mode) return grad_op(input, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def get_dim(self, name): if name == 'input_': return self.input_dim if name == 'output': return tuple(DownsampleFactorMax.out_shape( self.input_dim, self.pooling_size, st=self.step, ignore_border=self.ignore_border, padding=self.padding))
def test_DownsampleFactorMax(self): rng = numpy.random.RandomState(utt.fetch_seed()) # generate random images maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3)) imval = rng.rand(4, 2, 16, 16) images = tensor.dtensor4() for maxpoolshp, ignore_border, mode in product( maxpoolshps, [True, False], ['max', 'average_inc_pad', 'average_exc_pad']): # print 'maxpoolshp =', maxpoolshp # print 'ignore_border =', ignore_border # Pure Numpy computation numpy_output_val = self.numpy_max_pool_2d(imval, maxpoolshp, ignore_border, mode=mode) output = max_pool_2d(images, maxpoolshp, ignore_border, mode=mode) f = function([ images, ], [ output, ]) output_val = f(imval) assert numpy.all(output_val == numpy_output_val) # DownsampleFactorMax op maxpool_op = DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, mode=mode)(images) f = function([images], maxpool_op) output_val = f(imval) utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMax(self): rng = numpy.random.RandomState(utt.fetch_seed()) # generate random images maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3)) imval = rng.rand(4, 10, 64, 64) images = tensor.dtensor4() for maxpoolshp in maxpoolshps: for ignore_border in [True, False]: #print 'maxpoolshp =', maxpoolshp #print 'ignore_border =', ignore_border # Pure Numpy computation numpy_output_val = self.numpy_max_pool_2d( imval, maxpoolshp, ignore_border) output = max_pool_2d(images, maxpoolshp, ignore_border) f = function([ images, ], [ output, ]) output_val = f(imval) assert numpy.all(output_val == numpy_output_val) #DownsampleFactorMax op maxpool_op = DownsampleFactorMax( maxpoolshp, ignore_border=ignore_border)(images) f = function([images], maxpool_op) output_val = f(imval) assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()
def get_dim(self, name): if name == 'input_': return self.input_dim if name == 'output': return tuple(DownsampleFactorMax.out_shape(self.input_dim, self.pooling_size, st=self.step))
def get_dim(self, name): if name == 'input_': return self.input_dim if name == 'output': return tuple(DownsampleFactorMax.out_shape( self.input_dim, self.pooling_size, st=self.step, ignore_border=self.ignore_border, padding=self.padding))
def mp(input, grad): out = DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, st=stride)(input) grad_op = DownsampleFactorMaxGrad( maxpoolshp, ignore_border=ignore_border, st=stride) return grad_op(input, out, grad)
def test_DownsampleFactorMaxGrad_grad_st(self): """checks the gradient of the gradient for the case that stride is used""" rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((1, 1), (3, 3), (5, 3)) stridesizes = ((1, 1), (3, 3), (5, 7)) imval = rng.rand(1, 2, 16, 16) for maxpoolshp in maxpoolshps: for ignore_border in [True, False]: for stride in stridesizes: grad_shape = DownsampleFactorMax.out_shape( imval.shape, maxpoolshp, ignore_border=ignore_border, st=stride) grad_val = rng.rand(*grad_shape) def mp(input, grad): out = DownsampleFactorMax( maxpoolshp, ignore_border=ignore_border, st=stride)(input) grad_op = MaxPoolGrad( maxpoolshp, ignore_border=ignore_border, st=stride) return grad_op(input, out, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def max_pool_3d(input, ds, ignore_border=False): # [n,c,x,y,z]以外の入力は受け付けない if input.ndim != 5: raise NotImplementedError( 'max_pool_3d requires a input [n, c, x, y, z]') # 入力次元 vid_dim = input.ndim # [y, z]フレームの次元数 frame_shape = input.shape[-2:] # バッチサイズ # フレーム次元以外の全ての次元の要素数を掛け合わせる batch_size = T.prod(input.shape[:-2]) # http://deeplearning.net/software/theano/library/tensor/basic.html#theano.tensor.shape_padright batch_size = T.shape_padright(batch_size, 1) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([ 1, ]), frame_shape), 'int32') input_4D = T.reshape(input, new_shape, ndim=4) op = DownsampleFactorMax((ds[1], ds[2]), ignore_border) output = op(input_4D) outshape = T.join(0, input.shape[:-2], output.shape[-2:]) out = T.reshape(output, outshape, ndim=input.ndim) shufl = (list(range(vid_dim - 3)) + [vid_dim - 2] + [vid_dim - 1] + [vid_dim - 3]) input_time = out.dimshuffle(shufl) vid_shape = input_time.shape[-2:] batch_size = T.prod(input_time.shape[:-2]) batch_size = T.shape_padright(batch_size, 1) new_shape = T.cast(T.join(0, batch_size, T.as_tensor([ 1, ]), vid_shape), 'int32') input_4D_time = T.reshape(input_time, new_shape, ndim=4) op = DownsampleFactorMax((1, ds[0]), ignore_border) outtime = op(input_4D_time) outshape = T.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 T.reshape(outtime, outshape, ndim=input.ndim).dimshuffle(shufl)
def test_downsample(): import random shps = [ (1, 1, 1, 12), (1, 1, 2, 2), (1, 1, 1, 1), (1,1,4,4), (1, 1, 10, 11), (1, 2, 2, 2), (3,5,4,4), (25, 1, 7, 7), (1, 1, 12, 12), (1, 1, 2, 14), (1, 1, 12, 14), (1, 1, 14, 14), (1, 1, 16, 16), (1, 1, 18, 18), (1, 1, 24, 24), (1, 6, 24, 24), (10, 1, 24, 24), (10, 6, 24, 24), (30, 6, 12, 12), (30, 2, 24, 24), (30, 6, 24, 24), (10, 10, 10, 11), (1,1,10,1025), (1,1,10,1023), (1,1,1025,10), (1,1,1023,10), ] numpy.random.RandomState(unittest_tools.fetch_seed()).shuffle(shps) for shp in shps: for ds in (2, 2), (3,2), (1,1): if ds[0] > shp[2]: continue if ds[1] > shp[3]: continue #GpuDownsampleFactorMax don't having more then 512 columns in the output tensor if float(shp[3])/ds[1]>512: continue for ignore_border in (True, False): print 'test_downsample', shp, ds, ignore_border ds_op = DownsampleFactorMax(ds, ignore_border=ignore_border) a = tcn.shared_constructor(my_rand(*shp), 'a') f = pfunc([], ds_op(tensor.as_tensor_variable(a)), mode=mode_with_gpu) f2 = pfunc([], ds_op(tensor.as_tensor_variable(a)), mode=mode_without_gpu) assert any([isinstance(node.op, tcn.blas.GpuDownsampleFactorMax) for node in f.maker.env.toposort()]) assert any([isinstance(node.op, DownsampleFactorMax) for node in f2.maker.env.toposort()]) assert numpy.allclose(f(),f2()) g = pfunc([], tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),a), mode=mode_with_gpu) g2 = pfunc([], tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),a), mode=mode_without_gpu) assert any([isinstance(node.op, tcn.blas.GpuDownsampleFactorMaxGrad) for node in g.maker.env.toposort()]) assert any([isinstance(node.op, DownsampleFactorMaxGrad) for node in g2.maker.env.toposort()]) assert numpy.allclose(g(),g2())
def mp(input, grad): out = DownsampleFactorMax( maxpoolsize, ignore_border=True, st=stridesize, padding=paddingsize, )(input) grad_op = MaxPoolGrad(maxpoolsize, ignore_border=True, st=stridesize, padding=paddingsize) return grad_op(input, out, grad)
def pool_output_shape_2d(input_shape, axes, pool_shape, strides, pads, ignore_border=True): """ compute output shape for a pool """ return tuple(DownsampleFactorMax.out_shape( imgshape=input_shape, ds=pool_shape, st=strides, ignore_border=ignore_border, padding=pads, ))
def pool_output_shape_2d(input_shape, axes, pool_shape, strides, pads, ignore_border=True): """ compute output shape for a pool """ return tuple( DownsampleFactorMax.out_shape( imgshape=input_shape, ds=pool_shape, st=strides, ignore_border=ignore_border, padding=pads, ))
def test_DownsampleFactorMaxGrad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((1, 1), (3, 2), (2, 3)) imval = rng.rand(2, 3, 3, 4) * 10.0 # more variance means numeric gradient will be more accurate for maxpoolshp in maxpoolshps: for ignore_border in [True, False]: # print 'maxpoolshp =', maxpoolshp # print 'ignore_border =', ignore_border # The shape of the gradient will be the shape of the output grad_shape = DownsampleFactorMax.out_shape(imval.shape, maxpoolshp, ignore_border=ignore_border) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): out = DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border)(input) grad_op = DownsampleFactorMaxGrad(maxpoolshp, ignore_border=ignore_border) return grad_op(input, out, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxStrideExtra(self): rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9)) stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1), (2, 3), (10, 10), (1, 1)) imvsizs = ((16, 16), (16, 16), (16, 16), (8, 5), (8, 5), (8, 5), (8, 5)) outputshps = ((4, 10, 4, 7), (4, 10, 5, 8), (4, 10, 2, 3), (4, 10, 3, 4), (4, 10, 2, 3), (4, 10, 2, 3), (4, 10, 4, 1), (4, 10, 4, 1), (4, 10, 3, 2), (4, 10, 4, 2), (4, 10, 1, 0), (4, 10, 1, 1), (4, 10, 0, 0), (4, 10, 1, 1)) images = tensor.dtensor4() for indx in numpy.arange(len(maxpoolshps)): imvsize = imvsizs[indx] imval = rng.rand(4, 10, imvsize[0], imvsize[1]) stride = stridesizes[indx] maxpoolshp = maxpoolshps[indx] for ignore_border, mode in product([True, False], ['max', 'sum', 'average_inc_pad', 'average_exc_pad']): indx_out = indx * 2 if not ignore_border: indx_out += 1 outputshp = outputshps[indx_out] # DownsampleFactorMax op numpy_output_val = \ self.numpy_max_pool_2d_stride(imval, maxpoolshp, ignore_border, stride, mode) assert numpy_output_val.shape == outputshp, ( "outshape is %s, calculated shape is %s" % (outputshp, numpy_output_val.shape)) maxpool_op = \ DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, st=stride, mode=mode)(images) f = function([images], maxpool_op) output_val = f(imval) utt.assert_allclose(output_val, numpy_output_val)
def test_AveragePoolGrad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) avgpoolshps = ((1, 1), (3, 2), (2, 3)) imval = rng.rand(2, 3, 3, 4) * 10.0 # more variance means numeric gradient will be more accurate for avgpoolshp in avgpoolshps: for ignore_border in [True, False]: for mode in ['sum', 'average_inc_pad', 'average_exc_pad']: # print 'maxpoolshp =', maxpoolshp # print 'ignore_border =', ignore_border # The shape of the gradient will be the shape of the output grad_shape = DownsampleFactorMax.out_shape( imval.shape, avgpoolshp, ignore_border=ignore_border) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): grad_op = AveragePoolGrad( avgpoolshp, ignore_border=ignore_border, mode=mode) return grad_op(input, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) avgpoolshps = ((1, 1), (3, 2), (2, 3)) imval = rng.rand(2, 3, 3, 4) * 10.0 # more variance means numeric gradient will be more accurate for avgpoolshp in avgpoolshps: for ignore_border in [True, False]: for mode in ['sum', 'average_inc_pad', 'average_exc_pad']: # print 'maxpoolshp =', maxpoolshp # print 'ignore_border =', ignore_border # The shape of the gradient will be the shape of the output grad_shape = DownsampleFactorMax.out_shape( imval.shape, avgpoolshp, ignore_border=ignore_border) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): grad_op = AveragePoolGrad( avgpoolshp, ignore_border=ignore_border, mode=mode) return grad_op(input, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxGrad_grad(self): rng = numpy.random.RandomState(utt.fetch_seed()) maxpoolshps = ((1, 1), (3, 2), (2, 3)) imval = rng.rand(2, 3, 3, 4) * 10.0 # more variance means numeric gradient will be more accurate for maxpoolshp in maxpoolshps: for ignore_border in [True, False]: # print 'maxpoolshp =', maxpoolshp # print 'ignore_border =', ignore_border # The shape of the gradient will be the shape of the output grad_shape = DownsampleFactorMax.out_shape( imval.shape, maxpoolshp, ignore_border=ignore_border) grad_val = rng.rand(*grad_shape) * 10.0 def mp(input, grad): out = DownsampleFactorMax( maxpoolshp, ignore_border=ignore_border)(input) grad_op = MaxPoolGrad(maxpoolshp, ignore_border=ignore_border) return grad_op(input, out, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMax(self): rng = numpy.random.RandomState(utt.fetch_seed()) # generate random images maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3)) imval = rng.rand(4, 2, 16, 16) images = tensor.dtensor4() for maxpoolshp, ignore_border, mode in product(maxpoolshps, [True, False], ['max', 'sum', 'average_inc_pad', 'average_exc_pad']): # print 'maxpoolshp =', maxpoolshp # print 'ignore_border =', ignore_border # Pure Numpy computation numpy_output_val = self.numpy_max_pool_2d(imval, maxpoolshp, ignore_border, mode=mode) output = max_pool_2d(images, maxpoolshp, ignore_border, mode=mode) f = function([images, ], [output, ]) output_val = f(imval) utt.assert_allclose(output_val, numpy_output_val) # DownsampleFactorMax op maxpool_op = DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, mode=mode)(images) output_shape = DownsampleFactorMax.out_shape(imval.shape, maxpoolshp, ignore_border=ignore_border) utt.assert_allclose(numpy.asarray(output_shape), numpy_output_val.shape) f = function([images], maxpool_op) output_val = f(imval) utt.assert_allclose(output_val, numpy_output_val)
def test_AveragePoolGrad_grad_st(self): """checks the gradient of the gradient for the case that stride is used""" rng = numpy.random.RandomState(utt.fetch_seed()) avgpoolshps = ((1, 1), (3, 3), (5, 3)) stridesizes = ((1, 1), (3, 3), (5, 7)) imval = rng.rand(1, 2, 16, 16) for avgpoolshp in avgpoolshps: for ignore_border in [True, False]: for mode in ['sum', 'average_inc_pad', 'average_exc_pad']: for stride in stridesizes: grad_shape = DownsampleFactorMax.out_shape( imval.shape, avgpoolshp, ignore_border=ignore_border, st=stride) grad_val = rng.rand(*grad_shape) def mp(input, grad): grad_op = AveragePoolGrad( avgpoolshp, ignore_border=ignore_border, st=stride, mode=mode) return grad_op(input, grad) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def init_net(num_of_classes, input_len, conv_params): """ Major initialize of the neural net is in this method. You can adjust convolutional window size for each layer, number of filters for each layer and all the cascade parameters for every layer. We also initialize and define weights for neural net. :param num_of_classes: number of classes :param input_len: read (sequence chunk) length :return: weights in param variable, X and Y matrices, cost function, update function and maxima prediction """ cwin1=4*6 # multiples of 4 because of data representation cwin2=3 cwin3=2 num_filters_1=32 / 2 # how many different filters to learn at each layer num_filters_2=48 / 2 num_filters_3=64 / 2 # size of convolution windows, for each layer different values can be used w = init_weights((num_filters_1, 1, 1, cwin1)) # first convolution, 32 filters, stack size 1, 1 rows, cwin1 columns w2 = init_weights((num_filters_2, num_filters_1, 1, cwin2)) # second convolution, 64 filters, stack size 32 (one stack for each filter from previous layer), 1 row, cwin2 columns w3 = init_weights((num_filters_3, num_filters_2, 1, cwin3)) # third convolution, 128 filters, stack size 64 (one stack for each filter from previous layes), 1 row, cwin3 columns print "#### CONVOLUTION PARAMETERS ####" print "cwin1 %d" % cwin1 print "cwin2 %d" % cwin2 print "cwin3 %d" % cwin3 print "num_filters_1 %d" % num_filters_1 print "num_filters_2 %d" % num_filters_2 print "num_filters_3 %d" % num_filters_3 # convolution: filters are moved by one position at a time, see parameter subsample=(1, 1) # # max pooling: # scaling the input before applying the maxpool filter and # displacement (stride) when sliding the max pool filters # l1 conv: es = input_len es = (es - cwin1 + 1) es = es / conv1_stride # l1 max_pool: es = DownsampleFactorMax.out_shape((1, es), (1, downscale1), st=(1, stride1))[1] # downscale for first layer print "l1 es:", es # l2 conv: es = (es - cwin2 + 1) # l2 max_pool: es = DownsampleFactorMax.out_shape((1, es), (1, downscale2), st=(1, stride2))[1] # downscale for second layer print "l2 es:", es # l3 conv: es = (es - cwin3 + 1) # l3 max_pool: es = DownsampleFactorMax.out_shape((1, es), (1, downscale3), st=(1, stride3))[1] # downscale for third layer print "l3 es:", es # downscaling is performed so that we correctly set number of filters in last layer w4 = init_weights((num_filters_3 * es, 500)) # fully conected last layer, connects the outputs of 128 filters to 500 (arbitrary) hidden nodes, which are then connected to the output nodes w_o = init_weights((500, num_of_classes)) # number of exptected classes # matrix types X = T.ftensor4() Y = T.fmatrix() noise_l1, noise_l2, noise_l3, noise_l4, noise_py_x = model(X, w, w2, w3, w4, 0.2, 0.5, w_o, conv_params) l1, l2, l3, l4, py_x = model(X, w, w2, w3, w4, 0., 0., w_o, conv_params) y_x = T.argmax(py_x, axis=1) # maxima predictions cost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y)) # classification matrix to optimize - maximize the value that is actually there and minimize the others params = [w, w2, w3, w4, w_o] updates = RMSprop(cost, params, lr=0.001) # update function return params, X, Y, cost, updates, y_x
def mp(input, grad): out = DownsampleFactorMax( maxpoolshp, ignore_border=ignore_border)(input) grad_op = MaxPoolGrad(maxpoolshp, ignore_border=ignore_border) return grad_op(input, out, grad)
def mp(input): return DownsampleFactorMax(maxpoolshp, ignore_border=ignore_border, st=stride, mode=mode)(input)
def max_pool_3d(input, ds, ignore_border=False): """ 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 if (ds[1] > 1) or (ds[2] > 1): # extract dimensions frame_shape = input.shape[-2:] # count the number of "leading" dimensions, store as dmatrix batch_size = tensor.prod(input.shape[:-2]) batch_size = tensor.shape_padright(batch_size, 1) # store as 4D tensor with shape: (batch_size,1,height,width) 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) else: out = input if ds[0] == 1: return out # 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-4]) shufl = (0, 2, 3, 4, 1) input_time = out.dimshuffle(shufl) # reset dimensions # vid_shape = input_time.shape[-2:] 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]) shufl = (0, 4, 1, 2, 3) return tensor.reshape(outtime, outshape, ndim=input.ndim).dimshuffle(shufl)
def test_downsample(): shps = [ (1, 1, 1, 12), (1, 1, 2, 2), (1, 1, 1, 1), (1, 1, 4, 4), (1, 1, 10, 11), (1, 2, 2, 2), (3, 5, 4, 4), (25, 1, 7, 7), (1, 1, 12, 12), (1, 1, 2, 14), (1, 1, 12, 14), (1, 1, 14, 14), (1, 1, 16, 16), (1, 1, 18, 18), (1, 1, 24, 24), (1, 6, 24, 24), (10, 1, 24, 24), (10, 6, 24, 24), (30, 6, 12, 12), (30, 2, 24, 24), (30, 6, 24, 24), (10, 10, 10, 11), (1, 1, 10, 1025), (1, 1, 10, 1023), (1, 1, 1025, 10), (1, 1, 1023, 10), (65536, 1, 10, 10), (1, 65536, 10, 10), ] numpy.random.RandomState(unittest_tools.fetch_seed()).shuffle(shps) for shp in shps: for ds in (2, 2), (3, 2), (1, 1): if ds[0] > shp[2]: continue if ds[1] > shp[3]: continue # GpuDownsampleFactorMax doesn't like having more than 512 columns # in the output tensor. if float(shp[3]) / ds[1] > 512: continue for ignore_border in (True, False): # print 'test_downsample', shp, ds, ignore_border ds_op = DownsampleFactorMax(ds, ignore_border=ignore_border) a = tcn.shared_constructor(my_rand(*shp), 'a') f = pfunc([], ds_op(tensor.as_tensor_variable(a)), mode=mode_with_gpu.excluding('cudnn')) f2 = pfunc([], ds_op(tensor.as_tensor_variable(a)), mode=mode_without_gpu) assert any([ isinstance(node.op, tcn.blas.GpuDownsampleFactorMax) for node in f.maker.fgraph.toposort() ]) assert any([ isinstance(node.op, DownsampleFactorMax) for node in f2.maker.fgraph.toposort() ]) assert numpy.allclose(f(), f2()) # The grad is too slow on GT220 GPU # This cause the computer to freeze... # Remove this when it gets optimized enough # This only bypass the last 2 checks # Those tests where passing in all Mode on a GTX470 if shp[0] > 30000 or shp[1] > 30000: continue g = pfunc([], tensor.grad( ds_op(tensor.as_tensor_variable(a)).sum(), a), mode=mode_with_gpu.excluding('cudnn')) g2 = pfunc([], tensor.grad( ds_op(tensor.as_tensor_variable(a)).sum(), a), mode=mode_without_gpu) assert any([ isinstance(node.op, tcn.blas.GpuDownsampleFactorMaxGrad) for node in g.maker.fgraph.toposort() ]) assert any([ isinstance(node.op, DownsampleFactorMaxGrad) for node in g2.maker.fgraph.toposort() ]) assert numpy.allclose(g(), g2()), shp ggf = gradient.Lop( tensor.grad((ds_op(tensor.as_tensor_variable(a))**2).sum(), a), a, a) ref_mode = copy.copy(mode_without_gpu) ref_mode.check_py_code = False gpu_mode = copy.copy(mode_with_gpu) gpu_mode.check_py_code = False gg = pfunc([], ggf, mode=gpu_mode) gg2 = pfunc([], ggf, mode=ref_mode) assert any([ isinstance(node.op, tcn.blas.GpuDownsampleFactorMaxGradGrad) for node in gg.maker.fgraph.toposort() ]) assert any([ isinstance(node.op, DownsampleFactorMaxGradGrad) for node in gg2.maker.fgraph.toposort() ]) assert numpy.allclose(gg(), gg2()), shp
def mp(input): return DownsampleFactorMax( maxpoolshp, ignore_border=ignore_border)(input)
def maxpool_2d(z, in_dim, poolsize, poolstride): z = max_pool_2d(z, ds=poolsize, st=poolstride) output_size = tuple(DownsampleFactorMax.out_shape(in_dim, poolsize, st=poolstride)) return z, output_size