Python array_ops.shape_n方法代码示例

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import shape_n [as 别名]
def _Conv2DGrad(op, grad):
  strides = op.get_attr("strides")
  padding = op.get_attr("padding")
  use_cudnn_on_gpu = op.get_attr("use_cudnn_on_gpu")
  data_format = op.get_attr("data_format")
  shape_0, shape_1 = array_ops.shape_n([op.inputs[0], op.inputs[1]])
  return [nn_ops.conv2d_backprop_input(shape_0,
                                       op.inputs[1],
                                       grad,
                                       strides,
                                       padding,
                                       use_cudnn_on_gpu,
                                       data_format),
          nn_ops.conv2d_backprop_filter(op.inputs[0],
                                        shape_1,
                                        grad,
                                        strides,
                                        padding,
                                        use_cudnn_on_gpu,
                                        data_format)] 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import shape_n [as 别名]
def _verify_compatible_image_shapes(img1, img2):
    """Checks if two image tensors are compatible for applying SSIM or PSNR.
    This function checks if two sets of images have ranks at least 3, and if the
    last three dimensions match.
    Args:
    img1: Tensor containing the first image batch.
    img2: Tensor containing the second image batch.
    Returns:
    A tuple containing: the first tensor shape, the second tensor shape, and a
    list of control_flow_ops.Assert() ops implementing the checks.
    Raises:
    ValueError: When static shape check fails.
    """
    shape1 = img1.get_shape().with_rank_at_least(3)
    shape2 = img2.get_shape().with_rank_at_least(3)
    shape1[-3:].assert_is_compatible_with(shape2[-3:])

    if shape1.ndims is not None and shape2.ndims is not None:
        for dim1, dim2 in zip(reversed(shape1.dims[:-3]),
                              reversed(shape2.dims[:-3])):
            if not (dim1 == 1 or dim2 == 1 or dim1.is_compatible_with(dim2)):
                raise ValueError('Two images are not compatible: %s and %s' % (shape1, shape2))

    shape1, shape2 = array_ops.shape_n([img1, img2])

    checks = []
    checks.append(control_flow_ops.Assert(
      math_ops.greater_equal(array_ops.size(shape1), 3),
      [shape1, shape2], summarize=10))
    checks.append(control_flow_ops.Assert(
      math_ops.reduce_all(math_ops.equal(shape1[-3:], shape2[-3:])),
      [shape1, shape2], summarize=10))
    return shape1, shape2, checks 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import shape_n [as 别名]
def _ssim_per_channel(img1, img2, max_val=1.0):
    filter_size = constant_op.constant(11, dtype=dtypes.int32)
    filter_sigma = constant_op.constant(1.5, dtype=img1.dtype)

    shape1, shape2 = array_ops.shape_n([img1, img2])
    checks = [
      control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal(
          shape1[-3:-1], filter_size)), [shape1, filter_size], summarize=8),
      control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal(
          shape2[-3:-1], filter_size)), [shape2, filter_size], summarize=8)]

    # Enforce the check to run before computation.
    with ops.control_dependencies(checks):
        img1 = array_ops.identity(img1)

    kernel = _fspecial_gauss(filter_size, filter_sigma)
    kernel = array_ops.tile(kernel, multiples=[1, 1, shape1[-1], 1])

    compensation = 1.0

    def reducer(x):
        shape = array_ops.shape(x)
        x = array_ops.reshape(x, shape=array_ops.concat([[-1], shape[-3:]], 0))
        y = nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
        return array_ops.reshape(y, array_ops.concat([shape[:-3],
                                                      array_ops.shape(y)[1:]], 0))

    luminance, cs = _ssim_helper(img1, img2, reducer, max_val, compensation)

    # Average over the second and the third from the last: height, width.
    axes = constant_op.constant([-3, -2], dtype=dtypes.int32)
    ssim_val = math_ops.reduce_mean(luminance * cs, axes)
    cs = math_ops.reduce_mean(cs, axes)
    luminance = math_ops.reduce_mean(luminance, axes)
    return ssim_val, cs, luminance