Python dnn.GpuDnnConvDesc方法代码示例

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def convolve(self, input, **kwargs):
        
        # Messy to have these imports here, but seems to allow for switching DNN off.
        from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
                                           host_from_gpu,
                                           gpu_contiguous, HostFromGpu,
                                           gpu_alloc_empty)
        from theano.sandbox.cuda.dnn import GpuDnnConvDesc, GpuDnnConv, GpuDnnConvGradI, dnn_conv, dnn_pool 
        # Straight outta Radford
        img = gpu_contiguous(input)
        kerns = gpu_contiguous(self.W)
        desc = GpuDnnConvDesc(border_mode=self.crop, subsample=self.stride,
                              conv_mode='conv')(gpu_alloc_empty(img.shape[0], kerns.shape[1], img.shape[2]*self.stride[0], img.shape[3]*self.stride[1]).shape, kerns.shape)
        out = gpu_alloc_empty(img.shape[0], kerns.shape[1], img.shape[2]*self.stride[0], img.shape[3]*self.stride[1])
        conved = GpuDnnConvGradI()(kerns, img, out, desc)

        return conved
        
# Minibatch discrimination layer from OpenAI's improved GAN techniques 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def deconv(X, w, subsample=(1, 1), border_mode=(0, 0), conv_mode='conv'):
    """
    sets up dummy convolutional forward pass and uses its grad as deconv
    currently only tested/working with same padding
    """
    img = gpu_contiguous(X)
    kerns = gpu_contiguous(w)
    desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
                          conv_mode=conv_mode)(gpu_alloc_empty(img.shape[0], kerns.shape[1], img.shape[2] * subsample[0], img.shape[3] * subsample[1]).shape, kerns.shape)
    out = gpu_alloc_empty(img.shape[0], kerns.shape[1], img.shape[2] * subsample[0], img.shape[3] * subsample[1])
    d_img = GpuDnnConvGradI()(kerns, img, out, desc)
    return d_img 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_dnn_conv_desc_merge():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img_shp = T.as_tensor_variable(
        numpy.asarray([2, 1, 8, 8]).astype('int64'))
    kern_shp = T.as_tensor_variable(
        numpy.asarray([3, 1, 2, 2]).astype('int64'))
    desc1 = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(2, 2),
                               conv_mode='conv')(img_shp, kern_shp)
    desc2 = dnn.GpuDnnConvDesc(border_mode='full', subsample=(1, 1),
                               conv_mode='cross')(img_shp, kern_shp)
    # CDataType is not DeepCopyable so this will crash if we don't use
    # borrow=True
    f = theano.function([], [theano.Out(desc1, borrow=True),
                             theano.Out(desc2, borrow=True)],
                        mode=mode_with_gpu)

    d1, d2 = f()

    # This will be the case if they are merged, which would be bad.
    assert d1 != d2

    desc1v2 = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(2, 2),
                                 conv_mode='conv')(img_shp, kern_shp)
    f = theano.function([], [theano.Out(desc1, borrow=True),
                             theano.Out(desc1v2, borrow=True)],
                        mode=mode_with_gpu)
    assert len([n for n in f.maker.fgraph.apply_nodes
                if isinstance(n.op, dnn.GpuDnnConvDesc)]) == 1

    # CDATA type don't equal even if they represent the same object
    # So we can't use debugmode with it.
    if theano.config.mode not in ["DebugMode", "DEBUG_MODE"]:
        d1, d2 = f()

        # They won't be equal if they aren't merged.
        assert d1 == d2 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_dnn_conv_merge():
    """This test that we merge correctly multiple dnn_conv.

    This can is more difficult due to GpuEmptyAlloc that aren't
    merged.

    """
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img_shp = [2, 5, 6, 8]
    kern_shp = [3, 5, 5, 6]
    img = T.ftensor4('img')
    kern = T.ftensor4('kern')
    out = T.ftensor4('out')
    desc = dnn.GpuDnnConvDesc(
        border_mode='valid')(img.shape, kern.shape)

    # Test forward op
    o1 = dnn.dnn_conv(img, kern)
    o2 = dnn.dnn_conv(img, kern)
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
               numpy.random.rand(*kern_shp).astype('float32'))
    topo = f.maker.fgraph.toposort()
    assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]) == 1

    # Test grad w op
    o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
    o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
    f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradW)]) == 1

    # Test grad i op
    o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
    o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
    f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradI)]) == 1 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_conv(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        img = T.ftensor4('img')
        kerns = T.ftensor4('kerns')
        out = T.ftensor4('out')
        img_val = numpy.asarray(
            numpy.random.rand(10, 2, 6, 4),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(8, 2, 4, 3),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1), (2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv.get_out_shape(img_val.shape, kern_vals.shape,
                                             border_mode=params[0],
                                             subsample=params[1]),
                dtype='float32')
            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, kerns.shape)
            conv = dnn.GpuDnnConv()(img, kerns, out, desc)
            self._compile_and_check(
                [img, kerns, out],
                [conv],
                [img_val, kern_vals, out_vals],
                dnn.GpuDnnConv
            ) 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_conv3d(self):
        if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
            raise SkipTest('"CuDNN 3D convolution requires CuDNN v2')
        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
        img = ftensor5('img')
        kerns = ftensor5('kerns')
        out = ftensor5('out')
        img_val = numpy.asarray(
            numpy.random.rand(10, 2, 6, 4, 11),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(8, 2, 4, 3, 1),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1, 1), (2, 2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
                                               border_mode=params[0],
                                               subsample=params[1]),
                dtype='float32')
            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, kerns.shape)
            conv = dnn.GpuDnnConv3d()(img, kerns, out, desc)
            self._compile_and_check(
                [img, kerns, out],
                [conv],
                [img_val, kern_vals, out_vals],
                dnn.GpuDnnConv3d
            ) 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_dnn_conv_grad():
    if not cuda.dnn.dnn_available() or dnn.version() == -1:
        raise SkipTest('alpha != 1.0 not supported in cudnn v1')

    b = 1
    c = 4
    f = 3
    ih = 2
    iw = 8
    kh = 2
    kw = 2
    img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
    kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
    out_val = numpy.random.random((b, f, ih - kw + 1,
                                   iw - kw + 1)).astype('float32')

    def dconv(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConv()(img, kern, out, desc, alpha=0.5, beta=0.75)

    def dconvi(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConvGradI()(kern, out, img, desc, alpha=-1.0,
                                     beta=0.0)

    def dconvw(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConvGradW()(img, out, kern, desc, alpha=0.75,
                                     beta=-1.0)

    utt.verify_grad(dconv, [img_val, kern_val, out_val], mode=mode_with_gpu)
    utt.verify_grad(dconvi, [img_val, kern_val, out_val], mode=mode_with_gpu)
    utt.verify_grad(dconvw, [img_val, kern_val, out_val], mode=mode_with_gpu) 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def deconv(X, w, subsample=(1, 1), border_mode=(0, 0), conv_mode='conv'):
    """ 
    sets up dummy convolutional forward pass and uses its grad as deconv
    currently only tested/working with same padding
    """
    img = gpu_contiguous(X)
    kerns = gpu_contiguous(w)
    desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
                          conv_mode=conv_mode)(gpu_alloc_empty(img.shape[0], kerns.shape[1], img.shape[2]*subsample[0], img.shape[3]*subsample[1]).shape, kerns.shape)
    out = gpu_alloc_empty(img.shape[0], kerns.shape[1], img.shape[2]*subsample[0], img.shape[3]*subsample[1])
    d_img = GpuDnnConvGradI()(kerns, img, out, desc)
    return d_img 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_dnn_conv_grad():
    if not cuda.dnn.dnn_available() or dnn.version() == -1:
        raise SkipTest('alpha != 1.0 not supported in cudnn v1')

    b = 1
    c = 4
    f = 3
    ih = 2
    iw = 8
    kh = 2
    kw = 2
    img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
    kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
    out_val = numpy.random.random((b, f, ih - kw + 1,
                                   iw - kw + 1)).astype('float32')

    def dconv(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConv()(img, kern, out, desc, alpha=0.5, beta=0.75)

    def dconvi(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConvGradI()(kern, out, img, desc, alpha=-1.0,
                                     beta=0.0)

    def dconvw(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConvGradW()(img, out, kern, desc, alpha=0.75,
                                     beta=-1.0)

    utt.verify_grad(dconv, [img_val, kern_val, out_val])
    utt.verify_grad(dconvi, [img_val, kern_val, out_val])
    utt.verify_grad(dconvw, [img_val, kern_val, out_val]) 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def __init__(self,
                 filters,
                 batch_size,
                 input_space,
                 output_axes=('b', 'c', 0, 1),
                 subsample=(1, 1),
                 border_mode='valid',
                 filters_shape=None,
                 message=''):

        assert batch_size is None or batch_size > 0
        self._input_space = input_space
        self._output_axes = output_axes
        self._subsample = tuple(subsample)
        self._border_mode = border_mode

        super(Cudnn2D, self).__init__(
            filters=filters,
            img_shape=(batch_size, input_space.num_channels,
                       input_space.shape[0], input_space.shape[1]),
            subsample=self._subsample,
            border_mode=border_mode,
            filters_shape=filters.get_value(borrow=True).shape,
            message=message
        )

        # conv_op has to be changed
        self._conv_op = GpuDnnConv()
        self._desc = GpuDnnConvDesc(border_mode=border_mode,
                                    subsample=self._subsample,
                                    conv_mode='conv') 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_dnn_conv_inplace():
    """This test that we have inplace work correctly even when
    GpuAllocEmpty get merged together.

    """
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img_shp = [2, 5, 6, 8]
    kern_shp = [3, 5, 5, 6]
    img = T.ftensor4('img')
    kern = T.ftensor4('kern')
    out = T.ftensor4('out')
    desc1 = dnn.GpuDnnConvDesc(border_mode='valid', conv_mode='conv')(
        img.shape, kern.shape)
    desc2 = dnn.GpuDnnConvDesc(
        border_mode='valid', conv_mode='cross')(img.shape, kern.shape)

    # Test forward op
    o1 = dnn.dnn_conv(img, kern, conv_mode='conv')
    o2 = dnn.dnn_conv(img, kern, conv_mode='cross')
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
               numpy.random.rand(*kern_shp).astype('float32'))
    topo = f.maker.fgraph.toposort()
    convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]
    assert len(convs) == 2
    assert all([node.op.inplace for node in convs])
    assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2

    # Test grad w op
    out = gpu_alloc_empty(*kern.shape)
    o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc1)
    o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc2)
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradW)]
    assert len(convs) == 2
    assert all([node.op.inplace for node in convs])
    assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2

    # Test grad i op
    out = gpu_alloc_empty(*img.shape)
    o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc1)
    o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc2)
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradI)]
    assert len(convs) == 2
    assert all([node.op.inplace for node in convs])
    assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_conv3d_gradw(self):
        if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
            raise SkipTest('"CuDNN 3D convolution requires CuDNN v2')
        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
        img = ftensor5('img')
        kerns = ftensor5('kerns')
        out = ftensor5('out')
        img_val = numpy.asarray(
            numpy.random.rand(9, 2, 4, 8, 13),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(11, 2, 3, 1, 4),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1, 1), (2, 2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
                                               border_mode=params[0],
                                               subsample=params[1]),
                dtype='float32')

            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, out.shape)
            conv_grad_w = dnn.GpuDnnConv3dGradW()(
                img,
                out,
                kerns,
                desc,
            )
            self._compile_and_check(
                [img, out, kerns],
                [conv_grad_w],
                [img_val, out_vals, kern_vals],
                dnn.GpuDnnConv3dGradW
            ) 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_conv_gradi(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        img = T.ftensor4('img')
        kerns = T.ftensor4('kerns')
        out = T.ftensor4('out')
        img_val = numpy.asarray(
            numpy.random.rand(3, 4, 5, 6),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(4, 14, 15, 16),
            dtype='float32'
        )

        for params in product(
            ['valid'],  # Should this work for 'full'?
            [(1, 1)],
            ['conv', 'cross']
        ):
            temp_kerns = kerns.dimshuffle(1, 0, 2, 3)
            shape = (
                img_val.shape[0], kern_vals.shape[1],
                img_val.shape[2] + kern_vals.shape[2] - 1,
                img_val.shape[3] + kern_vals.shape[3] - 1
            )
            out_vals = numpy.zeros(shape, dtype='float32')
            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(out.shape, temp_kerns.shape)
            conv_grad_i = dnn.GpuDnnConvGradI()(
                temp_kerns,
                img,
                out,
                desc,
            )
            self._compile_and_check(
                [temp_kerns, img, out],
                [conv_grad_i],
                [kern_vals, img_val, out_vals],
                dnn.GpuDnnConvGradI
            ) 

# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import GpuDnnConvDesc [as 别名]
def test_conv3d_gradi(self):
        if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
            raise SkipTest('"CuDNN 3D convolution requires CuDNN v2')
        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
        img = ftensor5('img')
        kerns = ftensor5('kerns')
        out = ftensor5('out')
        img_val = numpy.asarray(
            numpy.random.rand(8, 4, 6, 7, 11),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(9, 4, 5, 1, 2),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1, 1), (2, 2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
                                               border_mode=params[0],
                                               subsample=params[1]),
                dtype='float32')

            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, kerns.shape)
            conv_grad_i = dnn.GpuDnnConv3dGradI()(
                kerns,
                out,
                img,
                desc,
            )
            self._compile_and_check(
                [kerns, out, img],
                [conv_grad_i],
                [kern_vals, out_vals, img_val],
                dnn.GpuDnnConv3dGradI
            )