Python abstract_conv.get_conv_output_shape函数代码示例

def CNN(x,c_l1,c_l2,f_l1,f_l2,PP,ims):
    print ims
    #-------
    #conv3D get rid of dependency of the number of input image channel
    b=numpy.zeros(c_l1.get_value().shape[0])
    conv1=tensor.nnet.relu(conv3D(x.dimshuffle(0,2,3,1,'x'),c_l1.dimshuffle(0,2,3,1,'x'),b,d=(1,1,1))) # shuffle dimensions
    conv1=tensor.sum(conv1,axis=3) #add the dimension of channels
    conv1=conv1.dimshuffle(0,3,1,2) #shuffle back to same dimension as conv2D
    #---------

    #conv1=tensor.nnet.relu(conv2d(x,c_l1)) #default stride=1 --subsample=(1,1) 
    conv1_shp=get_conv_output_shape(ims,c_l1.get_value().shape,border_mode='valid',subsample=(1,1))
    print  conv1_shp

    #pp=tensor.reshape(conv1,conv1_shp[:2]+(conv1_shp[2]*conv1_shp[3],))
    #print pp 

    pool1=pool_2d(conv1,(2,2),st=(2,2),ignore_border=True)  #default maxpool
    pool1_shp=get_pool_output_shape(conv1_shp,pool_size=(2,2),st=(2,2),ignore_border=True)
    print pool1_shp

    conv2=tensor.nnet.relu(conv2d(pool1,c_l2))
    conv2_shp=get_conv_output_shape(pool1_shp,c_l2.get_value().shape,border_mode='valid',subsample=(1,1))   
    print conv2_shp

    #pool2=pool_2d(conv2,(2,2),st=(2,2),ignore_border=True)
    pool2=spp(conv2,conv2_shp,PP,'max')

    fpool2=tensor.flatten(pool2,outdim=2)

    full1=tensor.nnet.relu(tensor.dot(fpool2,f_l1))
    pyx=tensor.nnet.softmax(tensor.dot(full1,f_l2))
    return c_l1, c_l2, f_l1, f_l2, pyx

def CNN(x,c_l1,c_l2,f_l1,f_l2,insize):
    print "in size ", insize
    conv1=tensor.nnet.relu(conv2d(x,c_l1)) #default stride=1 --subsample=(1,1) 
    conv1_shp=get_conv_output_shape(insize,c_l1.get_value().shape,border_mode='valid',subsample=(1,1))
    print "conv1 size ", conv1_shp
    pool1=pool_2d(conv1,(3,3),st=(3,3),ignore_border=True)  #default maxpool
    pool1_shp=get_pool_output_shape(conv1_shp,pool_size=(3,3),st=(3,3),ignore_border=True)
    print "pool1 size ", pool1_shp
    lrn1=LRN(pool1,pool1_shp)
    lrn1_shp=tuple(pool1_shp)
    print "cross map norm1 size ", lrn1_shp
    conv2=tensor.nnet.relu(conv2d(lrn1,c_l2))
    conv2_shp=get_conv_output_shape(lrn1_shp,c_l2.get_value().shape,border_mode='valid',subsample=(1,1))
    print "conv2 size ", conv2_shp 
    pool2=pool_2d(conv2,(2,2),st=(2,2),ignore_border=True)
    pool2_shp=get_pool_output_shape(conv2_shp,pool_size=(2,2),st=(2,2),ignore_border=True)
    print "pool2 size ", pool2_shp
    lrn2=LRN(pool2,pool2_shp)
    lrn2_shp=tuple(pool2_shp)
    print "cross map norm2 size " , lrn2_shp
    fpool2=tensor.flatten(lrn2,outdim=2)

    full1=tensor.nnet.relu(tensor.dot(fpool2,f_l1))
    pyx=tensor.nnet.sigmoid(tensor.dot(full1,f_l2))

    return c_l1, c_l2, f_l1, f_l2, pyx

    def test_basic(self):
        image_shape, kernel_shape = (3, 2, 8, 9), (4, 2, 5, 6)
        sub_sample = (1, 2)
        test1_params = get_conv_output_shape(
            image_shape, kernel_shape, 'valid', sub_sample)
        test2_params = get_conv_output_shape(
            image_shape, kernel_shape, 'half', sub_sample)
        test3_params = get_conv_output_shape(
            image_shape, kernel_shape, 'full', sub_sample)
        test4_params = get_conv_output_shape(
            image_shape, kernel_shape, (1, 2), sub_sample)

        self.assertTrue(test1_params == (3, 4, 4, 2))
        self.assertTrue(test2_params == (3, 4, 8, 5))
        self.assertTrue(test3_params == (3, 4, 12, 7))
        self.assertTrue(test4_params == (3, 4, 6, 4))

def burn():
    sz = 128
    img_shp = [sz, sz, sz, sz]
    kern_shp = [sz // 2, sz, 3, 3]
    out_shp = get_conv_output_shape(img_shp, kern_shp, 'valid', (1, 1))
    img = T.tensor4('img')
    kern = T.tensor4('kern')
    out = T.tensor4('out')

    def rand(shp):
        return np.random.rand(*shp).astype(theano.config.floatX)

    img = theano.shared(rand(img_shp))
    kern = theano.shared(rand(kern_shp))
    out = theano.shared(rand(out_shp))
    # beta 1 is needed to force the reuse of out, otherwise, it is
    # replaced by a GpuAllocEmpty
    o1 = dnn._dnn_conv(img, kern, conv_mode='conv', out=out, beta=1.)
    mode = theano.compile.get_default_mode().including(
        "local_remove_all_assert")
    f = theano.function([], [o1], mode=mode)
    theano.printing.debugprint(f)
    print("Start computation")
    for i in range(10000):
        f.fn()
    print("Computation stopped")

    def test_basic_3d(self):
        image_shape, kernel_shape = (3, 2, 12, 9, 7), (4, 2, 5, 6, 4)
        sub_sample = (1, 2, 1)
        filter_dilation = (2, 1, 1)
        test1_params = get_conv_output_shape(
            image_shape, kernel_shape, 'valid', sub_sample, filter_dilation)
        test2_params = get_conv_output_shape(
            image_shape, kernel_shape, 'half', sub_sample, filter_dilation)
        test3_params = get_conv_output_shape(
            image_shape, kernel_shape, 'full', sub_sample, filter_dilation)
        test4_params = get_conv_output_shape(
            image_shape, kernel_shape, (1, 2, 3), sub_sample, filter_dilation)

        self.assertTrue(test1_params == (3, 4, 4, 2, 4))
        self.assertTrue(test2_params == (3, 4, 12, 5, 8))
        self.assertTrue(test3_params == (3, 4, 20, 7, 10))
        self.assertTrue(test4_params == (3, 4, 6, 4, 10))

    def get_out_shape(ishape, kshape, border_mode, subsample):
        """
        This function computes the output shape for a convolution with
        the specified parameters. `ishape` and `kshape` can be symbolic
        or scalar.

        """
        return get_conv_output_shape(ishape, kshape, border_mode, subsample)

 def infer_shape(self, node, input_shape):
     imshp = input_shape[0]
     kshp = input_shape[1]
     res = get_conv_output_shape(
         imshp,
         kshp,
         self.border_mode,
         self.subsample)
     return [res]

def get_conv_shape(input_shape, filter_shape, padding, stride):
    """
    Helper method to calculate the shapes post-convolution operation given input parameters. This isn't used
    for our output_size calculations because Theano provides a function specific to its conv op.
    """
    if isinstance(input_shape, Iterable):
        shape = get_conv_output_shape(input_shape, filter_shape, padding, stride)
    else:
        shape = get_conv_shape_1axis(input_shape, filter_shape, padding, stride)
    return shape

 def get_if_valid_conv_output_shape(case_tuple):
     # Filter function to keep only cases that produce valid convolution output shapes.
     out_shp = get_conv_output_shape(case_tuple[0],  # input shape
                                     case_tuple[1],  # filter shape
                                     case_tuple[4],  # border mode
                                     case_tuple[2],  # subsample
                                     case_tuple[3])  # dilation
     try:
         return assert_conv_shape(out_shp)
     except ValueError:
         return False

def CNN(x,c_l1,c_l2,f_l1,f_l2,PP,ims):
    print ims
    conv1=tensor.nnet.relu(conv2d(x,c_l1)) #default stride=1 --subsample=(1,1) 
    conv1_shp=get_conv_output_shape(ims,c_l1.get_value().shape,border_mode='valid',subsample=(1,1))
    print  conv1_shp
    pp=tensor.reshape(conv1,conv1_shp[:2]+(conv1_shp[2]*conv1_shp[3],))
    print pp 
    pool1=pool_2d(conv1,(2,2),st=(2,2),ignore_border=True)  #default maxpool
    pool1_shp=get_pool_output_shape(conv1_shp,pool_size=(2,2),st=(2,2),ignore_border=True)
    print pool1_shp
    conv2=tensor.nnet.relu(conv2d(pool1,c_l2))
    conv2_shp=get_conv_output_shape(pool1_shp,c_l2.get_value().shape,border_mode='valid',subsample=(1,1))   
    print conv2_shp
    #pool2=pool_2d(conv2,(2,2),st=(2,2),ignore_border=True)
    pool2=spp(conv2,conv2_shp,PP,'max')

    fpool2=tensor.flatten(pool2,outdim=2)

    full1=tensor.nnet.relu(tensor.dot(fpool2,f_l1))
    pyx=tensor.nnet.sigmoid(tensor.dot(full1,f_l2))
    return c_l1, c_l2, f_l1, f_l2, pyx

    def _build(self, input_tensor):
        """Build 2D conolution operation of the input tensor

        Parameters
        ----------
        input_tensor : Tensor
            4D Tensor with shape (batch, #input channel, row, col)

        Returns
        -------
        Tensor
            4D Tensor with shape (batch, #output channel, row, col)
        """
        input_shape = input_tensor.shape
        _LG.debug('    input_shape: %s', input_shape)

        if not len(input_shape) == 4:
            raise ValueError(
                'Input tensor must be 4D. ({})'.format(input_tensor))

        border_mode = _map_border_mode(self.args['padding'])
        subsample = _get_subsample(self.args['strides'])
        filter_shape = self._get_filter_shape(input_shape[1])
        bias_shape = (filter_shape[0],)
        output_shape = get_conv_output_shape(
            input_shape, filter_shape, border_mode, subsample)
        _check_output_shape(input_shape, filter_shape, border_mode, subsample)

        _LG.debug('    border_mode: %s', border_mode)
        _LG.debug('    subsample: %s', subsample)
        _LG.debug('    filter_shape: %s', filter_shape)
        _LG.debug('    output_shape: %s', output_shape)

        self._build_parameters(filter_shape, bias_shape, input_tensor.dtype)

        filters = self.get_parameter_variable('filter')
        output_tensor = T.nnet.conv2d(
            input_tensor.unwrap(), filters=filters.unwrap(),
            input_shape=input_shape, filter_shape=filter_shape,
            border_mode=border_mode, subsample=subsample)

        if self.args['with_bias']:
            bias = self.get_parameter_variable('bias').unwrap()
            bias = bias.dimshuffle(('x', 0, 'x', 'x'))
            output_tensor = bias + output_tensor

        return wrapper.Tensor(output_tensor, shape=output_shape, name='output')

def local_conv2d_gradinputs_cpu(node):
    if not isinstance(node.op, AbstractConv2d_gradInputs):
        return None

    kern, topgrad, shape = node.inputs

    if ((not isinstance(kern.type, TensorType) or
         not isinstance(topgrad.type, TensorType))):
        return None
    if node.op.border_mode not in ['full', 'valid']:
        return None
    if not node.op.filter_flip:
        # Not tested yet
        return None

    # Conv 3d implementation, needed when subsample > 2
    if node.op.border_mode == 'valid' and node.op.subsample != (1, 1):
        kern = kern[:, :, ::-1, ::-1]
        shuffled_kern = kern.dimshuffle(0, 2, 3, 'x', 1)
        shuffled_topgrad = topgrad.dimshuffle(0, 2, 3, 'x', 1)
        b = theano.tensor.zeros_like(shuffled_kern[0, 0, 0, 0, :])
        rval = convTransp3D(W=shuffled_kern, b=b,
                            d=(node.op.subsample[0], node.op.subsample[1], 1),
                            H=shuffled_topgrad,
                            RShape=(shape[0], shape[1], 1))
        copy_stack_trace(node.outputs[0], rval)
        rval = theano.tensor.addbroadcast(rval, 3)
        rval = rval.dimshuffle(0, 4, 1, 2)
        rval = theano.tensor.patternbroadcast(rval,
                                              node.outputs[0].broadcastable)

        copy_stack_trace(node.outputs[0], rval)
        return [rval]

    # Conv2d Implementation
    dx, dy = node.op.subsample
    if dx not in (1, 2) or dy not in (1, 2):
        # Not implemented in the gradient of ConvOp
        return None

    if node.op.imshp is None:
        op_imshp = (None, None, None, None)
    else:
        op_imshp = node.op.imshp

    if node.op.kshp is None:
        op_kshp = (None, None, None, None)
    else:
        op_kshp = node.op.kshp

    if None in op_imshp or None in op_kshp:
        if (dx, dy) != (1, 1):
            return None

    mode = 'valid'
    if not node.op.border_mode == 'full':
        mode = 'full'
    filters = kern.dimshuffle((1, 0, 2, 3))
    filters = filters[:, :, ::-1, ::-1]

    outshp = get_conv_output_shape(op_imshp, op_kshp,
                                   node.op.border_mode, node.op.subsample)[2:]
    fulloutshp = get_conv_output_shape(op_imshp, op_kshp,
                                       node.op.border_mode, (1, 1))[2:]

    nkern = op_imshp[1]
    imshp = (op_kshp[0], outshp[0], outshp[1])
    imshp_logical = (op_kshp[0], fulloutshp[0], fulloutshp[1])
    din = ConvOp(imshp,
                 op_kshp[2:],
                 nkern,
                 op_imshp[0],
                 1, 1, output_mode=mode,
                 unroll_batch=None, unroll_kern=None,
                 unroll_patch=None,
                 imshp_logical=imshp_logical,
                 kshp_logical=None,
                 version=-1,
                 direction_hint='bprop inputs')
    din = din(topgrad, filters)
    copy_stack_trace(node.outputs[0], din)
    din = theano.tensor.patternbroadcast(din, node.outputs[0].broadcastable)
    copy_stack_trace(node.outputs[0], din)
    return [din]

def local_conv2d_gradweight_cpu(node):
    if not isinstance(node.op, AbstractConv2d_gradWeights):
        return None

    img, topgrad, shape = node.inputs

    if ((not isinstance(img.type, TensorType) or
         not isinstance(topgrad.type, TensorType))):
        return None
    if node.op.border_mode not in ['full', 'valid']:
        return None
    if not node.op.filter_flip:
        # Not tested yet
        return

    if node.op.border_mode == 'valid' and \
            (node.op.subsample != (1, 1)):
        # Use the gradient as defined in conv3D, because the implementation
        # by Conv is slow (about 3x slower than conv3D, and probably 10x
        # slower than it could be), and incorrect when subsample > 2.
        # build a "node", that should be equivalent to the one given by
        # self.make_node, but using convGrad3D instead.
        shuffled_img = img.dimshuffle(0, 2, 3, 'x', 1)
        shuffled_topgrad = topgrad.dimshuffle(0, 2, 3, 'x', 1)
        rval = convGrad3D(V=shuffled_img,
                          d=(node.op.subsample[0], node.op.subsample[1], 1),
                          WShape=(shuffled_topgrad.shape[4],
                                  shape[0], shape[1], 1,
                                  shuffled_img.shape[4]),
                          dCdH=shuffled_topgrad)
        copy_stack_trace(node.outputs[0], rval)

        rval = theano.tensor.addbroadcast(rval, 3)
        rval = rval.dimshuffle(0, 4, 1, 2)
        rval = rval[:, :, ::-1, ::-1]
        rval = theano.tensor.patternbroadcast(rval,
                                              node.outputs[0].broadcastable)
        copy_stack_trace(node.outputs[0], rval)
        return [rval]

    dx, dy = node.op.subsample
    if dx not in (1, 2) or dy not in (1, 2):
        # Not implemented in the gradient of ConvOp
        return None

    if node.op.imshp is None:
        op_imshp = (None, None, None, None)
    else:
        op_imshp = node.op.imshp

    if node.op.kshp is None:
        op_kshp = (None, None, None, None)
    else:
        op_kshp = node.op.kshp

    if None in op_imshp or None in op_kshp:
        if (dx, dy) != (1, 1):
            # We cannot infer the shapes
            return None

    # Determine gradient on kernels
    assert len(op_imshp) == 4 and len(op_kshp) == 4

    outshp = get_conv_output_shape(op_imshp, op_kshp,
                                   node.op.border_mode, node.op.subsample)[2:]
    fulloutshp = get_conv_output_shape(op_imshp, op_kshp,
                                       node.op.border_mode, (1, 1))[2:]

    newimg = img.dimshuffle((1, 0, 2, 3))
    newtopgrad = topgrad.dimshuffle((1, 0, 2, 3))

    if node.op.border_mode == 'valid':
        (img, filters) = (newimg, newtopgrad)
        kshp_logical = fulloutshp
        kshp_logical_top_aligned = False
        imshp_logical = None
        (bsize, nkern) = (op_imshp[1], op_kshp[0])
        imshp = (op_imshp[0], op_imshp[2], op_imshp[3])
        kshp = outshp
    elif node.op.border_mode == 'full':
        (img, filters) = (newtopgrad, newimg)
        kshp_logical = None
        kshp_logical_top_aligned = True
        imshp_logical = (op_imshp[0],
                         fulloutshp[0],
                         fulloutshp[1])
        (bsize, nkern) = (op_kshp[0], op_imshp[1])
        imshp = (op_imshp[0], outshp[0], outshp[1])
        kshp = op_imshp[2:]
    else:
        raise NotImplementedError(
            'Only [full,valid] modes are currently supported.')

    # Flip the kernels
    filters = filters[:, :, ::-1, ::-1]

    dw = ConvOp(imshp, kshp, nkern, bsize, 1, 1, output_mode='valid',
                unroll_batch=None, unroll_kern=None, unroll_patch=None,
                imshp_logical=imshp_logical,
                kshp_logical=kshp_logical,
#.........这里部分代码省略.........

    elif test == BWD_FILTER:
        check_config = cudnn.bwd_filter_algo_supports_dtype_config(args.algo, args.dtype, args.precision, ndim)
    elif test == BWD_DATA:
        check_config = cudnn.bwd_data_algo_supports_dtype_config(args.algo, args.dtype, args.precision, ndim)
    if not check_config:
        print('Warning: %s computation does not normally support configuration (%s, %s) for algo %s.' % (
            test, args.dtype, args.precision, args.algo), file=sys.stderr)

algo = args.algo
dtype = args.dtype
precision = args.precision
parameters = (
    args.input_shape, args.filter_shape, args.subsample, args.dilation, args.border_mode, args.conv_mode,
    args.alpha, args.beta)
if args.print_infos:
    CheckDnn.print_infos(count_tests=False)
print('======================')
print('Running', test, algo, dtype, precision, *parameters)
if test == FWD:
    tests.run_conv_fwd(algo, dtype, precision, parameters)
    expected_output_shape = get_conv_output_shape(args.input_shape, args.filter_shape, args.border_mode,
                                                  args.subsample, args.dilation)
elif test == BWD_FILTER:
    tests.run_conv_gradweight(algo, dtype, precision, parameters)
    expected_output_shape = args.filter_shape
elif test == BWD_DATA:
    tests.run_conv_gradinput(algo, dtype, precision, parameters)
    expected_output_shape = args.input_shape
print('Computed shape:', expected_output_shape)
print('... OK')

 def array_like_conv_output(self, inputs_shape, filters_shape, border_mode, subsample, dilation, dtype):
     # Return a random array with inferred convolution output shape.
     out_shp = get_conv_output_shape(inputs_shape, filters_shape, border_mode, subsample, dilation)
     out_shp = assert_conv_shape(out_shp)
     return np.random.random(out_shp).astype(dtype)