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Python filter_acts.FilterActs类代码示例

本文整理汇总了Python中pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs的典型用法代码示例。如果您正苦于以下问题:Python FilterActs类的具体用法?Python FilterActs怎么用?Python FilterActs使用的例子?那么, 这里精选的类代码示例或许可以为您提供帮助。


在下文中一共展示了FilterActs类的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: lmul

    def lmul(self, x):
        """
        dot(x, A)
        aka, do convolution with input image x

        """

        check_cuda(str(type(self)) + ".lmul")
        # TODO Why is it CPU??
        print "Por que?!?!", type(x)
        cpu = "Cuda" not in str(type(x))
        if cpu:
            x = gpu_from_host(x)

        assert x.ndim == 5
        x_axes = self.input_axes
        assert len(x_axes) == 5

        op_axes = ("c", 0, 1, "t", "b")
        if tuple(x_axes) != op_axes:
            print "ssssssssssssssss"
            x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])

        _x_4d_shape = (
            self.signal_shape[0],
            self.signal_shape[1],
            self.signal_shape[2],
            self.signal_shape[3] * self.signal_shape[4],
        )

        x = x.reshape(_x_4d_shape)

        x = gpu_contiguous(x)

        rval = FilterActs(self.pad, self.partial_sum, self.kernel_stride[0])(x, self._filters)

        if cpu:
            rval = host_from_gpu(rval)

        rval = rval.reshape(
            (
                self.filter_shape[3],
                self.filter_shape[4],
                rval.shape[1],
                rval.shape[2],
                self.signal_shape[3],
                self.signal_shape[4],
            )
        )

        rval = diagonal_subtensor(rval, 4, 0).sum(axis=0)

        # Format the output based on the output space
        rval_axes = self.output_axes
        assert len(rval_axes) == 5

        if tuple(rval_axes) != op_axes:
            rval = rval.dimshuffle(*[op_axes.index(axis) for axis in rval_axes])

        return rval
开发者ID:YangXS,项目名称:lisa_emotiw,代码行数:60,代码来源:conv3d_c01tb.py

示例2: make_funcs

def make_funcs(batch_size, rows, cols, channels, filter_rows, num_filters):
    rng = np.random.RandomState([2012, 10, 9])

    filter_cols = filter_rows

    base_image_value = rng.uniform(-1.0, 1.0, (channels, rows, cols, batch_size)).astype("float32")
    base_filters_value = rng.uniform(-1.0, 1.0, (channels, filter_rows, filter_cols, num_filters)).astype("float32")
    images = shared(base_image_value)
    filters = shared(base_filters_value, name="filters")

    # bench.py should always be run in gpu mode so we should not need a gpu_from_host here
    output = FilterActs()(images, filters)

    output_shared = shared(output.eval())

    cuda_convnet = function([], updates={output_shared: output})
    cuda_convnet.name = "cuda_convnet"

    images_bc01v = base_image_value.transpose(3, 0, 1, 2)
    filters_bc01v = base_filters_value.transpose(3, 0, 1, 2)
    filters_bc01v = filters_bc01v[:, :, ::-1, ::-1]

    images_bc01 = shared(images_bc01v)
    filters_bc01 = shared(filters_bc01v)

    output_conv2d = conv2d(
        images_bc01, filters_bc01, border_mode="valid", image_shape=images_bc01v.shape, filter_shape=filters_bc01v.shape
    )

    output_conv2d_shared = shared(output_conv2d.eval())

    baseline = function([], updates={output_conv2d_shared: output_conv2d})
    baseline.name = "baseline"

    return cuda_convnet, baseline
开发者ID:CandyPythonFlow,项目名称:pylearn2,代码行数:35,代码来源:bench.py

示例3: test_match_valid_conv_strided

def test_match_valid_conv_strided():

    # Tests that running FilterActs with stride is the same as running
    # theano's conv2D in valid mode and then downsampling

    rng = np.random.RandomState([2012,10,9])

    batch_size = 5
    rows = 9
    cols = 9
    channels = 3
    filter_rows = 3
    filter_cols = filter_rows
    stride = 3
    num_filters = 16

    images = shared(rng.uniform(-1., 1., (channels, rows, cols,
        batch_size)).astype('float32'), name='images')
    filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
        filter_cols, num_filters)).astype('float32'), name='filters')

    gpu_images = gpu_from_host(images)
    gpu_filters = gpu_from_host(filters)

    output = FilterActs(stride=stride)(gpu_images, gpu_filters)
    output = host_from_gpu(output)

    images_bc01 = images.dimshuffle(3,0,1,2)
    filters_bc01 = filters.dimshuffle(3,0,1,2)
    filters_bc01 = filters_bc01[:,:,::-1,::-1]

    output_conv2d = conv2d(images_bc01, filters_bc01,
            border_mode='valid', subsample=(stride, stride))

    output_conv2d_orig = output_conv2d.dimshuffle(1,2,3,0)
    output_conv2d = output_conv2d_orig  # [:, ::stride, ::stride, :]
    f = function([], [output, output_conv2d, output_conv2d_orig])

    output, output_conv2d, output_conv2d_orig = f()

    warnings.warn("""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
                     One possibility is that theano is numerically unstable and Alex's code is better.
                     Probably theano CPU 64 bit is OK but it's worth checking the others.""")
    if np.abs(output - output_conv2d).max() > 2.4e-6:
        assert type(output) == type(output_conv2d)
        assert output.dtype == output_conv2d.dtype
        if output.shape != output_conv2d.shape:
            print 'cuda-convnet shape: ',output.shape
            print 'theano shape: ',output_conv2d.shape
            assert False
        err = np.abs(output - output_conv2d)
        print 'absolute error range: ', (err.min(), err.max())
        print 'mean absolute error: ', err.mean()
        print 'cuda-convnet value range: ', (output.min(), output.max())
        print 'theano value range: ', (output_conv2d.min(), output_conv2d.max())
        assert False
开发者ID:Alienfeel,项目名称:pylearn2,代码行数:56,代码来源:test_filter_acts.py

示例4: test_grad

def test_grad():

    rng = np.random.RandomState([2012, 10, 9])

    batch_size = 5
    rows = 10
    cols = 9
    channels = 3
    filter_rows = 4
    filter_cols = filter_rows
    num_filters = 16

    images = shared(rng.uniform(-1.0, 1.0, (channels, rows, cols, batch_size)).astype("float32"), name="images")
    filters = shared(
        rng.uniform(-1.0, 1.0, (channels, filter_rows, filter_cols, num_filters)).astype("float32"), name="filters"
    )

    gpu_images = gpu_from_host(images)
    gpu_filters = gpu_from_host(filters)

    output = FilterActs()(gpu_images, gpu_filters)
    output = host_from_gpu(output)
    # XXX: use verify_grad
    output_grad = grad(output.sum(), images)

    images_bc01 = images.dimshuffle(3, 0, 1, 2)
    filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
    filters_bc01 = filters_bc01[:, :, ::-1, ::-1]

    output_conv2d = conv2d(images_bc01, filters_bc01, border_mode="valid")

    output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)
    # XXX: use verify_grad
    output_conv2d_grad = grad(output_conv2d.sum(), images)
    f = function([], [output_grad, output_conv2d_grad])

    output_grad, output_conv2d_grad = f()

    warnings.warn(
        """test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
                     One possibility is that theano is numerically unstable and Alex's code is better.
                     Probably theano CPU 64 bit is OK but it's worth checking the others."""
    )
    if np.abs(output_grad - output_conv2d_grad).max() > 7.7e-6:
        assert type(output_grad) == type(output_conv2d_grad)
        assert output_grad.dtype == output_conv2d_grad.dtype
        if output_grad.shape != output_conv2d_grad.shape:
            print "cuda-convnet shape: ", output_grad.shape
            print "theano shape: ", output_conv2d_grad.shape
            assert False
        err = np.abs(output_grad - output_conv2d_grad)
        print "absolute error range: ", (err.min(), err.max())
        print "mean absolute error: ", err.mean()
        print "cuda-convnet value range: ", (output_grad.min(), output_grad.max())
        print "theano value range: ", (output_conv2d_grad.min(), output_conv2d_grad.max())
        assert False
开发者ID:gbcolborne,项目名称:pylearn2,代码行数:56,代码来源:test_filter_acts.py

示例5: test_match_valid_conv

def test_match_valid_conv():

    # Tests that running FilterActs with no padding is the same as running
    # theano's conv2D in valid mode

    rng = np.random.RandomState([2012,10,9])

    batch_size = 5
    rows = 10
    cols = 9
    channels = 3
    filter_rows = 4
    filter_cols = filter_rows
    num_filters = 16

    images = shared(rng.uniform(-1., 1., (channels, rows, cols,
        batch_size)).astype('float32'), name='images')
    filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
        filter_cols, num_filters)).astype('float32'), name='filters')

    gpu_images = gpu_from_host(images)
    gpu_filters = gpu_from_host(filters)

    output = FilterActs()(gpu_images, gpu_filters)
    output = host_from_gpu(output)

    images_bc01 = images.dimshuffle(3,0,1,2)
    filters_bc01 = filters.dimshuffle(3,0,1,2)
    filters_bc01 = filters_bc01[:,:,::-1,::-1]

    output_conv2d = conv2d(images_bc01, filters_bc01,
            border_mode='valid')

    output_conv2d = output_conv2d.dimshuffle(1,2,3,0)

    try:
        f = function([], [output, output_conv2d])
    except:
        raise KnownFailureTest("cuda-convnet code depends on an unmerged theano feature.")

    output, output_conv2d = f()

    warnings.warn("test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?")
    if np.abs(output - output_conv2d).max() > 2.4e-6:
        assert type(output) == type(output_conv2d)
        assert output.dtype == output_conv2d.dtype
        if output.shape != output_conv2d.shape:
            print 'cuda-convnet shape: ',output.shape
            print 'theano shape: ',output_conv2d.shape
            assert False
        err = np.abs(output - output_conv2d)
        print 'absolute error range: ', (err.min(), err.max())
        print 'mean absolute error: ', err.mean()
        print 'cuda-convnet value range: ', (output.min(), output.max())
        print 'theano value range: ', (output_conv2d.min(), output_conv2d.max())
        assert False
开发者ID:deigen,项目名称:pylearn,代码行数:56,代码来源:test_filter_acts.py

示例6: lmul

    def lmul(self, x):
        """
        .. todo::

            WRITEME properly

        dot(x, A)
        aka, do convolution with input image x
        """

        check_cuda(str(type(self)) + ".lmul")

        cpu = 'Cuda' not in str(type(x))

        if cpu:
            x = gpu_from_host(x)

        # x must be formatted as channel, topo dim 0, topo dim 1, batch_index
        # for use with FilterActs
        assert x.ndim == 4
        x_axes = self.input_axes
        assert len(x_axes) == 4

        op_axes = ('c', 0, 1, 'b')

        if tuple(x_axes) != op_axes:
            x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])

        x = gpu_contiguous(x)

        # Patch old pickle files.
        if not hasattr(self, 'kernel_stride'):
            self.kernel_stride = (1, 1)
        rval = FilterActs(self.pad, self.partial_sum, self.kernel_stride[0])(
            x,
            self._filters
        )

        # Format the output based on the output space
        rval_axes = self.output_axes
        assert len(rval_axes) == 4

        if cpu:
            rval = host_from_gpu(rval)

        if tuple(rval_axes) != op_axes:
            rval = rval.dimshuffle(*[op_axes.index(axis)
                                     for axis in rval_axes])

        return rval
开发者ID:CURG,项目名称:pylearn2,代码行数:50,代码来源:conv2d_c01b.py

示例7: lmul

    def lmul(self, x):
        """
        dot(x, A)
        aka, do convolution with input image x

        """

        cpu = 'Cuda' not in str(type(x))

        if cpu:
            x = gpu_from_host(x)

        # x must be formatted as channel, topo dim 0, topo dim 1, batch_index
        # for use with FilterActs
        assert x.ndim == 4
        x_axes = self.input_axes
        assert len(x_axes) == 4

        op_axes = ('c', 0, 1, 'b')

        if tuple(x_axes) != op_axes:
            x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])

        x = gpu_contiguous(x)

        rval = FilterActs(self.pad, self.partial_sum)(x, self._filters)

        # Format the output based on the output space
        rval_axes = self.output_axes
        assert len(rval_axes) == 4

        if tuple(rval_axes) != op_axes:
            rval = rval.dimshuffle(*[op_axes.index(axis) for axis in rval_axes])

        if cpu:
            rval = host_from_gpu(rval)

        return rval
开发者ID:casperkaae,项目名称:pylearn2,代码行数:38,代码来源:conv2d_c01b.py

示例8: test_match_grad_valid_conv

def test_match_grad_valid_conv():

    # Tests that weightActs is the gradient of FilterActs
    # with respect to the weights.

    for partial_sum in [0, 1, 4]:
        rng = np.random.RandomState([2012, 10, 9])

        batch_size = 3
        rows = 7
        cols = 9
        channels = 8
        filter_rows = 4
        filter_cols = filter_rows
        num_filters = 16

        images = shared(rng.uniform(-1., 1., (channels, rows, cols,
                                              batch_size)).astype('float32'),
                        name='images')
        filters = rng.uniform(-1., 1.,
                              (channels, filter_rows,
                               filter_cols, num_filters)).astype('float32')
        filters = shared(filters, name='filters')

        gpu_images = gpu_from_host(images)
        gpu_filters = gpu_from_host(filters)

        output = FilterActs(partial_sum=partial_sum)(gpu_images, gpu_filters)
        output = host_from_gpu(output)

        images_bc01 = images.dimshuffle(3, 0, 1, 2)
        filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
        filters_bc01 = filters_bc01[:, :, ::-1, ::-1]

        output_conv2d = conv2d(images_bc01, filters_bc01,
                               border_mode='valid')

        output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)

        theano_rng = MRG_RandomStreams(2013 + 1 + 31)

        coeffs = theano_rng.normal(avg=0., std=1.,
                                   size=output_conv2d.shape, dtype='float32')

        cost_conv2d = (coeffs * output_conv2d).sum()

        weights_grad_conv2d = T.grad(cost_conv2d, filters)

        cost = (coeffs * output).sum()
        hid_acts_grad = T.grad(cost, output)

        weights_grad = WeightActs(partial_sum=partial_sum)(
            gpu_images,
            gpu_from_host(hid_acts_grad),
            as_tensor_variable((4, 4))
        )[0]
        weights_grad = host_from_gpu(weights_grad)

        f = function([], [output, output_conv2d, weights_grad,
                          weights_grad_conv2d])

        output, output_conv2d, weights_grad, weights_grad_conv2d = f()

        if np.abs(output - output_conv2d).max() > 8e-6:
            assert type(output) == type(output_conv2d)
            assert output.dtype == output_conv2d.dtype
            if output.shape != output_conv2d.shape:
                print('cuda-convnet shape: ', output.shape)
                print('theano shape: ', output_conv2d.shape)
                assert False
            err = np.abs(output - output_conv2d)
            print('absolute error range: ', (err.min(), err.max()))
            print('mean absolute error: ', err.mean())
            print('cuda-convnet value range: ', (output.min(), output.max()))
            print('theano value range: ', (output_conv2d.min(),
                                           output_conv2d.max()))
            assert False

        warnings.warn(
            "test_match_grad_valid_conv success criterion is not very strict."
            " Can we verify that this is OK? One possibility is that theano"
            " is numerically unstable and Alex's code is better. Probably"
            " theano CPU 64 bit is OK but it's worth checking the others.")

        if np.abs(weights_grad - weights_grad_conv2d).max() > 8.6e-6:
            if type(weights_grad) != type(weights_grad_conv2d):
                raise AssertionError("weights_grad is of type " +
                                     str(weights_grad))
            assert weights_grad.dtype == weights_grad_conv2d.dtype
            if weights_grad.shape != weights_grad_conv2d.shape:
                print('cuda-convnet shape: ', weights_grad.shape)
                print('theano shape: ', weights_grad_conv2d.shape)
                assert False
            err = np.abs(weights_grad - weights_grad_conv2d)
            print('absolute error range: ', (err.min(), err.max()))
            print('mean absolute error: ', err.mean())
            print('cuda-convnet value range: ', (weights_grad.min(),
                                                 weights_grad.max()))
            print('theano value range: ', (weights_grad_conv2d.min(),
                                           weights_grad_conv2d.max()))
#.........这里部分代码省略.........
开发者ID:123fengye741,项目名称:pylearn2,代码行数:101,代码来源:test_weight_acts.py

示例9: test_match_valid_conv_padded

def test_match_valid_conv_padded():

    # Tests that running FilterActs with no padding is the same as running
    # theano's conv2D in valid mode

    rng = np.random.RandomState([2012,10,9])

    batch_size = 5
    rows = 10
    cols = 9
    channels = 3
    filter_rows = 4
    filter_cols = filter_rows
    num_filters = 16

    images = shared(rng.uniform(-1., 1., (channels, rows, cols,
        batch_size)).astype('float32'), name='images')
    filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
        filter_cols, num_filters)).astype('float32'), name='filters')

    gpu_images = gpu_from_host(images)
    gpu_filters = gpu_from_host(filters)

    PAD = 3

    output = FilterActs(PAD)(gpu_images, gpu_filters)
    output = host_from_gpu(output)

    images_bc01 = T.alloc(0., batch_size, channels, rows + PAD * 2, cols + PAD * 2)

    images_bc01 = T.set_subtensor(images_bc01[:,:,PAD:-PAD,PAD:-PAD], images.dimshuffle(3,0,1,2))


    filters_bc01 = filters.dimshuffle(3,0,1,2)
    filters_bc01 = filters_bc01[:,:,::-1,::-1]

    output_conv2d = conv2d(images_bc01, filters_bc01,
            border_mode='valid')

    output_conv2d = output_conv2d.dimshuffle(1,2,3,0)

    f = function([], [output, output_conv2d])

    output, output_conv2d = f()

    warnings.warn("""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
                     One possibility is that theano is numerically unstable and Alex's code is better.
                     Probably theano CPU 64 bit is OK but it's worth checking the others.""")

    assert output.shape == output_conv2d.shape

    if np.abs(output - output_conv2d).max() > 2.4e-6:
        assert type(output) == type(output_conv2d)
        assert output.dtype == output_conv2d.dtype
        if output.shape != output_conv2d.shape:
            print('cuda-convnet shape: ',output.shape)
            print('theano shape: ',output_conv2d.shape)
            assert False
        err = np.abs(output - output_conv2d)
        print('absolute error range: ', (err.min(), err.max()))
        print('mean absolute error: ', err.mean())
        print('cuda-convnet value range: ', (output.min(), output.max()))
        print('theano value range: ', (output_conv2d.min(), output_conv2d.max()))
        assert False
开发者ID:123fengye741,项目名称:pylearn2,代码行数:64,代码来源:test_filter_acts.py


注:本文中的pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。