datasets.py 文件源码

def do_center_crop(value, size, name=None):
    """Randomly crops a tensor to a given size.
    Slices a shape `size` portion out of `value` at a uniformly chosen offset.
    Requires `value.shape >= size`.
    If a dimension should not be cropped, pass the full size of that dimension.
    For example, RGB images can be cropped with
    `size = [crop_height, crop_width, 3]`.
    Args:
        value: Input tensor to crop.
        size: 1-D tensor with size the rank of `value`.
        seed: Python integer. Used to create a random seed. See
            [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
            for behavior.
        name: A name for this operation (optional).
    Returns:
        A cropped tensor of the same rank as `value` and shape `size`.
    """
    # TODO(shlens): Implement edge case to guarantee output size dimensions.
    # If size > value.shape, zero pad the result so that it always has shape
    # exactly size.
    from tensorflow.python.framework import dtypes
    with ops.op_scope([value, size], name, "center_crop") as name:
        value = ops.convert_to_tensor(value, name="value")
        size = ops.convert_to_tensor(size, dtype=dtypes.int32, name="size")
        shape = array_ops.shape(value)
        check = logging_ops.Assert(
                math_ops.reduce_all(shape >= size),
                ["Need value.shape >= size, got ", shape, size])
        shape = control_flow_ops.with_dependencies([check], shape)
        limit = shape - size + 1
        offset = tf.random_uniform(
                array_ops.shape(shape),
                dtype=size.dtype,
                maxval=size.dtype.max,
                seed=0) % limit
        offset2 = shape // 2 - size // 2
        #import ipdb; ipdb.set_trace()
        return array_ops.slice(value, offset, size, name=name)