960 строки
40 KiB
Python
960 строки
40 KiB
Python
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""A module for helper tensorflow ops."""
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import math
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import numpy as np
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import six
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import tensorflow as tf
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from core import box_list
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from core import box_list_ops
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from core import standard_fields as fields
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from utils import shape_utils
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from utils import static_shape
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def expanded_shape(orig_shape, start_dim, num_dims):
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"""Inserts multiple ones into a shape vector.
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Inserts an all-1 vector of length num_dims at position start_dim into a shape.
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Can be combined with tf.reshape to generalize tf.expand_dims.
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Args:
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orig_shape: the shape into which the all-1 vector is added (int32 vector)
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start_dim: insertion position (int scalar)
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num_dims: length of the inserted all-1 vector (int scalar)
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Returns:
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An int32 vector of length tf.size(orig_shape) + num_dims.
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"""
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with tf.name_scope('ExpandedShape'):
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start_dim = tf.expand_dims(start_dim, 0) # scalar to rank-1
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before = tf.slice(orig_shape, [0], start_dim)
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add_shape = tf.ones(tf.reshape(num_dims, [1]), dtype=tf.int32)
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after = tf.slice(orig_shape, start_dim, [-1])
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new_shape = tf.concat([before, add_shape, after], 0)
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return new_shape
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def normalized_to_image_coordinates(normalized_boxes, image_shape,
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parallel_iterations=32):
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"""Converts a batch of boxes from normal to image coordinates.
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Args:
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normalized_boxes: a float32 tensor of shape [None, num_boxes, 4] in
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normalized coordinates.
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image_shape: a float32 tensor of shape [4] containing the image shape.
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parallel_iterations: parallelism for the map_fn op.
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Returns:
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absolute_boxes: a float32 tensor of shape [None, num_boxes, 4] containg the
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boxes in image coordinates.
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"""
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def _to_absolute_coordinates(normalized_boxes):
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return box_list_ops.to_absolute_coordinates(
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box_list.BoxList(normalized_boxes),
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image_shape[1], image_shape[2], check_range=False).get()
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absolute_boxes = shape_utils.static_or_dynamic_map_fn(
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_to_absolute_coordinates,
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elems=(normalized_boxes),
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dtype=tf.float32,
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parallel_iterations=parallel_iterations,
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back_prop=True)
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return absolute_boxes
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def meshgrid(x, y):
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"""Tiles the contents of x and y into a pair of grids.
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Multidimensional analog of numpy.meshgrid, giving the same behavior if x and y
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are vectors. Generally, this will give:
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xgrid(i1, ..., i_m, j_1, ..., j_n) = x(j_1, ..., j_n)
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ygrid(i1, ..., i_m, j_1, ..., j_n) = y(i_1, ..., i_m)
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Keep in mind that the order of the arguments and outputs is reverse relative
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to the order of the indices they go into, done for compatibility with numpy.
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The output tensors have the same shapes. Specifically:
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xgrid.get_shape() = y.get_shape().concatenate(x.get_shape())
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ygrid.get_shape() = y.get_shape().concatenate(x.get_shape())
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Args:
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x: A tensor of arbitrary shape and rank. xgrid will contain these values
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varying in its last dimensions.
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y: A tensor of arbitrary shape and rank. ygrid will contain these values
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varying in its first dimensions.
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Returns:
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A tuple of tensors (xgrid, ygrid).
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"""
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with tf.name_scope('Meshgrid'):
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x = tf.convert_to_tensor(x)
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y = tf.convert_to_tensor(y)
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x_exp_shape = expanded_shape(tf.shape(x), 0, tf.rank(y))
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y_exp_shape = expanded_shape(tf.shape(y), tf.rank(y), tf.rank(x))
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xgrid = tf.tile(tf.reshape(x, x_exp_shape), y_exp_shape)
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ygrid = tf.tile(tf.reshape(y, y_exp_shape), x_exp_shape)
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new_shape = y.get_shape().concatenate(x.get_shape())
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xgrid.set_shape(new_shape)
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ygrid.set_shape(new_shape)
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return xgrid, ygrid
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def fixed_padding(inputs, kernel_size, rate=1):
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"""Pads the input along the spatial dimensions independently of input size.
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Args:
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inputs: A tensor of size [batch, height_in, width_in, channels].
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kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
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Should be a positive integer.
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rate: An integer, rate for atrous convolution.
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Returns:
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output: A tensor of size [batch, height_out, width_out, channels] with the
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input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
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"""
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kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
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pad_total = kernel_size_effective - 1
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pad_beg = pad_total // 2
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pad_end = pad_total - pad_beg
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padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
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[pad_beg, pad_end], [0, 0]])
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return padded_inputs
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def pad_to_multiple(tensor, multiple):
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"""Returns the tensor zero padded to the specified multiple.
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Appends 0s to the end of the first and second dimension (height and width) of
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the tensor until both dimensions are a multiple of the input argument
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'multiple'. E.g. given an input tensor of shape [1, 3, 5, 1] and an input
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multiple of 4, PadToMultiple will append 0s so that the resulting tensor will
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be of shape [1, 4, 8, 1].
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Args:
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tensor: rank 4 float32 tensor, where
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tensor -> [batch_size, height, width, channels].
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multiple: the multiple to pad to.
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Returns:
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padded_tensor: the tensor zero padded to the specified multiple.
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"""
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tensor_shape = tensor.get_shape()
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batch_size = static_shape.get_batch_size(tensor_shape)
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tensor_height = static_shape.get_height(tensor_shape)
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tensor_width = static_shape.get_width(tensor_shape)
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tensor_depth = static_shape.get_depth(tensor_shape)
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if batch_size is None:
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batch_size = tf.shape(tensor)[0]
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if tensor_height is None:
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tensor_height = tf.shape(tensor)[1]
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padded_tensor_height = tf.to_int32(
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tf.ceil(tf.to_float(tensor_height) / tf.to_float(multiple))) * multiple
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else:
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padded_tensor_height = int(
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math.ceil(float(tensor_height) / multiple) * multiple)
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if tensor_width is None:
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tensor_width = tf.shape(tensor)[2]
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padded_tensor_width = tf.to_int32(
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tf.ceil(tf.to_float(tensor_width) / tf.to_float(multiple))) * multiple
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else:
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padded_tensor_width = int(
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math.ceil(float(tensor_width) / multiple) * multiple)
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if tensor_depth is None:
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tensor_depth = tf.shape(tensor)[3]
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# Use tf.concat instead of tf.pad to preserve static shape
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if padded_tensor_height != tensor_height:
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height_pad = tf.zeros([
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batch_size, padded_tensor_height - tensor_height, tensor_width,
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tensor_depth
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])
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tensor = tf.concat([tensor, height_pad], 1)
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if padded_tensor_width != tensor_width:
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width_pad = tf.zeros([
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batch_size, padded_tensor_height, padded_tensor_width - tensor_width,
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tensor_depth
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])
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tensor = tf.concat([tensor, width_pad], 2)
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return tensor
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def padded_one_hot_encoding(indices, depth, left_pad):
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"""Returns a zero padded one-hot tensor.
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This function converts a sparse representation of indices (e.g., [4]) to a
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zero padded one-hot representation (e.g., [0, 0, 0, 0, 1] with depth = 4 and
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left_pad = 1). If `indices` is empty, the result will simply be a tensor of
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shape (0, depth + left_pad). If depth = 0, then this function just returns
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`None`.
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Args:
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indices: an integer tensor of shape [num_indices].
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depth: depth for the one-hot tensor (integer).
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left_pad: number of zeros to left pad the one-hot tensor with (integer).
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Returns:
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padded_onehot: a tensor with shape (num_indices, depth + left_pad). Returns
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`None` if the depth is zero.
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Raises:
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ValueError: if `indices` does not have rank 1 or if `left_pad` or `depth are
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either negative or non-integers.
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TODO(rathodv): add runtime checks for depth and indices.
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"""
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if depth < 0 or not isinstance(depth, six.integer_types):
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raise ValueError('`depth` must be a non-negative integer.')
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if left_pad < 0 or not isinstance(left_pad, six.integer_types):
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raise ValueError('`left_pad` must be a non-negative integer.')
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if depth == 0:
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return None
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rank = len(indices.get_shape().as_list())
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if rank != 1:
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raise ValueError('`indices` must have rank 1, but has rank=%s' % rank)
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def one_hot_and_pad():
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one_hot = tf.cast(tf.one_hot(tf.cast(indices, tf.int64), depth,
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on_value=1, off_value=0), tf.float32)
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return tf.pad(one_hot, [[0, 0], [left_pad, 0]], mode='CONSTANT')
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result = tf.cond(tf.greater(tf.size(indices), 0), one_hot_and_pad,
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lambda: tf.zeros((depth + left_pad, 0)))
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return tf.reshape(result, [-1, depth + left_pad])
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def dense_to_sparse_boxes(dense_locations, dense_num_boxes, num_classes):
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"""Converts bounding boxes from dense to sparse form.
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Args:
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dense_locations: a [max_num_boxes, 4] tensor in which only the first k rows
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are valid bounding box location coordinates, where k is the sum of
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elements in dense_num_boxes.
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dense_num_boxes: a [max_num_classes] tensor indicating the counts of
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various bounding box classes e.g. [1, 0, 0, 2] means that the first
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bounding box is of class 0 and the second and third bounding boxes are
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of class 3. The sum of elements in this tensor is the number of valid
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bounding boxes.
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num_classes: number of classes
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Returns:
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box_locations: a [num_boxes, 4] tensor containing only valid bounding
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boxes (i.e. the first num_boxes rows of dense_locations)
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box_classes: a [num_boxes] tensor containing the classes of each bounding
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box (e.g. dense_num_boxes = [1, 0, 0, 2] => box_classes = [0, 3, 3]
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"""
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num_valid_boxes = tf.reduce_sum(dense_num_boxes)
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box_locations = tf.slice(dense_locations,
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tf.constant([0, 0]), tf.stack([num_valid_boxes, 4]))
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tiled_classes = [tf.tile([i], tf.expand_dims(dense_num_boxes[i], 0))
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for i in range(num_classes)]
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box_classes = tf.concat(tiled_classes, 0)
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box_locations.set_shape([None, 4])
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return box_locations, box_classes
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def indices_to_dense_vector(indices,
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size,
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indices_value=1.,
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default_value=0,
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dtype=tf.float32):
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"""Creates dense vector with indices set to specific value and rest to zeros.
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This function exists because it is unclear if it is safe to use
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tf.sparse_to_dense(indices, [size], 1, validate_indices=False)
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with indices which are not ordered.
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This function accepts a dynamic size (e.g. tf.shape(tensor)[0])
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Args:
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indices: 1d Tensor with integer indices which are to be set to
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indices_values.
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size: scalar with size (integer) of output Tensor.
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indices_value: values of elements specified by indices in the output vector
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default_value: values of other elements in the output vector.
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dtype: data type.
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Returns:
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dense 1D Tensor of shape [size] with indices set to indices_values and the
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rest set to default_value.
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"""
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size = tf.to_int32(size)
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zeros = tf.ones([size], dtype=dtype) * default_value
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values = tf.ones_like(indices, dtype=dtype) * indices_value
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return tf.dynamic_stitch([tf.range(size), tf.to_int32(indices)],
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[zeros, values])
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def reduce_sum_trailing_dimensions(tensor, ndims):
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"""Computes sum across all dimensions following first `ndims` dimensions."""
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return tf.reduce_sum(tensor, axis=tuple(range(ndims, tensor.shape.ndims)))
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def retain_groundtruth(tensor_dict, valid_indices):
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"""Retains groundtruth by valid indices.
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Args:
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tensor_dict: a dictionary of following groundtruth tensors -
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fields.InputDataFields.groundtruth_boxes
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fields.InputDataFields.groundtruth_classes
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fields.InputDataFields.groundtruth_keypoints
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fields.InputDataFields.groundtruth_instance_masks
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fields.InputDataFields.groundtruth_is_crowd
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fields.InputDataFields.groundtruth_area
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fields.InputDataFields.groundtruth_label_types
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fields.InputDataFields.groundtruth_difficult
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valid_indices: a tensor with valid indices for the box-level groundtruth.
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Returns:
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a dictionary of tensors containing only the groundtruth for valid_indices.
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Raises:
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ValueError: If the shape of valid_indices is invalid.
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ValueError: field fields.InputDataFields.groundtruth_boxes is
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not present in tensor_dict.
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"""
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input_shape = valid_indices.get_shape().as_list()
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if not (len(input_shape) == 1 or
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(len(input_shape) == 2 and input_shape[1] == 1)):
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raise ValueError('The shape of valid_indices is invalid.')
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valid_indices = tf.reshape(valid_indices, [-1])
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valid_dict = {}
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if fields.InputDataFields.groundtruth_boxes in tensor_dict:
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||
|
|
# Prevents reshape failure when num_boxes is 0.
|
||
|
|
num_boxes = tf.maximum(tf.shape(
|
||
|
|
tensor_dict[fields.InputDataFields.groundtruth_boxes])[0], 1)
|
||
|
|
for key in tensor_dict:
|
||
|
|
if key in [fields.InputDataFields.groundtruth_boxes,
|
||
|
|
fields.InputDataFields.groundtruth_classes,
|
||
|
|
fields.InputDataFields.groundtruth_keypoints,
|
||
|
|
fields.InputDataFields.groundtruth_instance_masks]:
|
||
|
|
valid_dict[key] = tf.gather(tensor_dict[key], valid_indices)
|
||
|
|
# Input decoder returns empty tensor when these fields are not provided.
|
||
|
|
# Needs to reshape into [num_boxes, -1] for tf.gather() to work.
|
||
|
|
elif key in [fields.InputDataFields.groundtruth_is_crowd,
|
||
|
|
fields.InputDataFields.groundtruth_area,
|
||
|
|
fields.InputDataFields.groundtruth_difficult,
|
||
|
|
fields.InputDataFields.groundtruth_label_types]:
|
||
|
|
valid_dict[key] = tf.reshape(
|
||
|
|
tf.gather(tf.reshape(tensor_dict[key], [num_boxes, -1]),
|
||
|
|
valid_indices), [-1])
|
||
|
|
# Fields that are not associated with boxes.
|
||
|
|
else:
|
||
|
|
valid_dict[key] = tensor_dict[key]
|
||
|
|
else:
|
||
|
|
raise ValueError('%s not present in input tensor dict.' % (
|
||
|
|
fields.InputDataFields.groundtruth_boxes))
|
||
|
|
return valid_dict
|
||
|
|
|
||
|
|
|
||
|
|
def retain_groundtruth_with_positive_classes(tensor_dict):
|
||
|
|
"""Retains only groundtruth with positive class ids.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tensor_dict: a dictionary of following groundtruth tensors -
|
||
|
|
fields.InputDataFields.groundtruth_boxes
|
||
|
|
fields.InputDataFields.groundtruth_classes
|
||
|
|
fields.InputDataFields.groundtruth_keypoints
|
||
|
|
fields.InputDataFields.groundtruth_instance_masks
|
||
|
|
fields.InputDataFields.groundtruth_is_crowd
|
||
|
|
fields.InputDataFields.groundtruth_area
|
||
|
|
fields.InputDataFields.groundtruth_label_types
|
||
|
|
fields.InputDataFields.groundtruth_difficult
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
a dictionary of tensors containing only the groundtruth with positive
|
||
|
|
classes.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: If groundtruth_classes tensor is not in tensor_dict.
|
||
|
|
"""
|
||
|
|
if fields.InputDataFields.groundtruth_classes not in tensor_dict:
|
||
|
|
raise ValueError('`groundtruth classes` not in tensor_dict.')
|
||
|
|
keep_indices = tf.where(tf.greater(
|
||
|
|
tensor_dict[fields.InputDataFields.groundtruth_classes], 0))
|
||
|
|
return retain_groundtruth(tensor_dict, keep_indices)
|
||
|
|
|
||
|
|
|
||
|
|
def replace_nan_groundtruth_label_scores_with_ones(label_scores):
|
||
|
|
"""Replaces nan label scores with 1.0.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
label_scores: a tensor containing object annoation label scores.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
a tensor where NaN label scores have been replaced by ones.
|
||
|
|
"""
|
||
|
|
return tf.where(
|
||
|
|
tf.is_nan(label_scores), tf.ones(tf.shape(label_scores)), label_scores)
|
||
|
|
|
||
|
|
|
||
|
|
def filter_groundtruth_with_crowd_boxes(tensor_dict):
|
||
|
|
"""Filters out groundtruth with boxes corresponding to crowd.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tensor_dict: a dictionary of following groundtruth tensors -
|
||
|
|
fields.InputDataFields.groundtruth_boxes
|
||
|
|
fields.InputDataFields.groundtruth_classes
|
||
|
|
fields.InputDataFields.groundtruth_keypoints
|
||
|
|
fields.InputDataFields.groundtruth_instance_masks
|
||
|
|
fields.InputDataFields.groundtruth_is_crowd
|
||
|
|
fields.InputDataFields.groundtruth_area
|
||
|
|
fields.InputDataFields.groundtruth_label_types
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
a dictionary of tensors containing only the groundtruth that have bounding
|
||
|
|
boxes.
|
||
|
|
"""
|
||
|
|
if fields.InputDataFields.groundtruth_is_crowd in tensor_dict:
|
||
|
|
is_crowd = tensor_dict[fields.InputDataFields.groundtruth_is_crowd]
|
||
|
|
is_not_crowd = tf.logical_not(is_crowd)
|
||
|
|
is_not_crowd_indices = tf.where(is_not_crowd)
|
||
|
|
tensor_dict = retain_groundtruth(tensor_dict, is_not_crowd_indices)
|
||
|
|
return tensor_dict
|
||
|
|
|
||
|
|
|
||
|
|
def filter_groundtruth_with_nan_box_coordinates(tensor_dict):
|
||
|
|
"""Filters out groundtruth with no bounding boxes.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tensor_dict: a dictionary of following groundtruth tensors -
|
||
|
|
fields.InputDataFields.groundtruth_boxes
|
||
|
|
fields.InputDataFields.groundtruth_classes
|
||
|
|
fields.InputDataFields.groundtruth_keypoints
|
||
|
|
fields.InputDataFields.groundtruth_instance_masks
|
||
|
|
fields.InputDataFields.groundtruth_is_crowd
|
||
|
|
fields.InputDataFields.groundtruth_area
|
||
|
|
fields.InputDataFields.groundtruth_label_types
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
a dictionary of tensors containing only the groundtruth that have bounding
|
||
|
|
boxes.
|
||
|
|
"""
|
||
|
|
groundtruth_boxes = tensor_dict[fields.InputDataFields.groundtruth_boxes]
|
||
|
|
nan_indicator_vector = tf.greater(tf.reduce_sum(tf.to_int32(
|
||
|
|
tf.is_nan(groundtruth_boxes)), reduction_indices=[1]), 0)
|
||
|
|
valid_indicator_vector = tf.logical_not(nan_indicator_vector)
|
||
|
|
valid_indices = tf.where(valid_indicator_vector)
|
||
|
|
|
||
|
|
return retain_groundtruth(tensor_dict, valid_indices)
|
||
|
|
|
||
|
|
|
||
|
|
def normalize_to_target(inputs,
|
||
|
|
target_norm_value,
|
||
|
|
dim,
|
||
|
|
epsilon=1e-7,
|
||
|
|
trainable=True,
|
||
|
|
scope='NormalizeToTarget',
|
||
|
|
summarize=True):
|
||
|
|
"""L2 normalizes the inputs across the specified dimension to a target norm.
|
||
|
|
|
||
|
|
This op implements the L2 Normalization layer introduced in
|
||
|
|
Liu, Wei, et al. "SSD: Single Shot MultiBox Detector."
|
||
|
|
and Liu, Wei, Andrew Rabinovich, and Alexander C. Berg.
|
||
|
|
"Parsenet: Looking wider to see better." and is useful for bringing
|
||
|
|
activations from multiple layers in a convnet to a standard scale.
|
||
|
|
|
||
|
|
Note that the rank of `inputs` must be known and the dimension to which
|
||
|
|
normalization is to be applied should be statically defined.
|
||
|
|
|
||
|
|
TODO(jonathanhuang): Add option to scale by L2 norm of the entire input.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: A `Tensor` of arbitrary size.
|
||
|
|
target_norm_value: A float value that specifies an initial target norm or
|
||
|
|
a list of floats (whose length must be equal to the depth along the
|
||
|
|
dimension to be normalized) specifying a per-dimension multiplier
|
||
|
|
after normalization.
|
||
|
|
dim: The dimension along which the input is normalized.
|
||
|
|
epsilon: A small value to add to the inputs to avoid dividing by zero.
|
||
|
|
trainable: Whether the norm is trainable or not
|
||
|
|
scope: Optional scope for variable_scope.
|
||
|
|
summarize: Whether or not to add a tensorflow summary for the op.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The input tensor normalized to the specified target norm.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: If dim is smaller than the number of dimensions in 'inputs'.
|
||
|
|
ValueError: If target_norm_value is not a float or a list of floats with
|
||
|
|
length equal to the depth along the dimension to be normalized.
|
||
|
|
"""
|
||
|
|
with tf.variable_scope(scope, 'NormalizeToTarget', [inputs]):
|
||
|
|
if not inputs.get_shape():
|
||
|
|
raise ValueError('The input rank must be known.')
|
||
|
|
input_shape = inputs.get_shape().as_list()
|
||
|
|
input_rank = len(input_shape)
|
||
|
|
if dim < 0 or dim >= input_rank:
|
||
|
|
raise ValueError(
|
||
|
|
'dim must be non-negative but smaller than the input rank.')
|
||
|
|
if not input_shape[dim]:
|
||
|
|
raise ValueError('input shape should be statically defined along '
|
||
|
|
'the specified dimension.')
|
||
|
|
depth = input_shape[dim]
|
||
|
|
if not (isinstance(target_norm_value, float) or
|
||
|
|
(isinstance(target_norm_value, list) and
|
||
|
|
len(target_norm_value) == depth) and
|
||
|
|
all([isinstance(val, float) for val in target_norm_value])):
|
||
|
|
raise ValueError('target_norm_value must be a float or a list of floats '
|
||
|
|
'with length equal to the depth along the dimension to '
|
||
|
|
'be normalized.')
|
||
|
|
if isinstance(target_norm_value, float):
|
||
|
|
initial_norm = depth * [target_norm_value]
|
||
|
|
else:
|
||
|
|
initial_norm = target_norm_value
|
||
|
|
target_norm = tf.contrib.framework.model_variable(
|
||
|
|
name='weights', dtype=tf.float32,
|
||
|
|
initializer=tf.constant(initial_norm, dtype=tf.float32),
|
||
|
|
trainable=trainable)
|
||
|
|
if summarize:
|
||
|
|
mean = tf.reduce_mean(target_norm)
|
||
|
|
mean = tf.Print(mean, ['NormalizeToTarget:', mean])
|
||
|
|
tf.summary.scalar(tf.get_variable_scope().name, mean)
|
||
|
|
lengths = epsilon + tf.sqrt(tf.reduce_sum(tf.square(inputs), dim, True))
|
||
|
|
mult_shape = input_rank*[1]
|
||
|
|
mult_shape[dim] = depth
|
||
|
|
return tf.reshape(target_norm, mult_shape) * tf.truediv(inputs, lengths)
|
||
|
|
|
||
|
|
|
||
|
|
def position_sensitive_crop_regions(image,
|
||
|
|
boxes,
|
||
|
|
box_ind,
|
||
|
|
crop_size,
|
||
|
|
num_spatial_bins,
|
||
|
|
global_pool,
|
||
|
|
extrapolation_value=None):
|
||
|
|
"""Position-sensitive crop and pool rectangular regions from a feature grid.
|
||
|
|
|
||
|
|
The output crops are split into `spatial_bins_y` vertical bins
|
||
|
|
and `spatial_bins_x` horizontal bins. For each intersection of a vertical
|
||
|
|
and a horizontal bin the output values are gathered by performing
|
||
|
|
`tf.image.crop_and_resize` (bilinear resampling) on a a separate subset of
|
||
|
|
channels of the image. This reduces `depth` by a factor of
|
||
|
|
`(spatial_bins_y * spatial_bins_x)`.
|
||
|
|
|
||
|
|
When global_pool is True, this function implements a differentiable version
|
||
|
|
of position-sensitive RoI pooling used in
|
||
|
|
[R-FCN detection system](https://arxiv.org/abs/1605.06409).
|
||
|
|
|
||
|
|
When global_pool is False, this function implements a differentiable version
|
||
|
|
of position-sensitive assembling operation used in
|
||
|
|
[instance FCN](https://arxiv.org/abs/1603.08678).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
image: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
|
||
|
|
`int16`, `int32`, `int64`, `half`, `float32`, `float64`.
|
||
|
|
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
|
||
|
|
Both `image_height` and `image_width` need to be positive.
|
||
|
|
boxes: A `Tensor` of type `float32`.
|
||
|
|
A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
|
||
|
|
specifies the coordinates of a box in the `box_ind[i]` image and is
|
||
|
|
specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized
|
||
|
|
coordinate value of `y` is mapped to the image coordinate at
|
||
|
|
`y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image
|
||
|
|
height is mapped to `[0, image_height - 1] in image height coordinates.
|
||
|
|
We do allow y1 > y2, in which case the sampled crop is an up-down flipped
|
||
|
|
version of the original image. The width dimension is treated similarly.
|
||
|
|
Normalized coordinates outside the `[0, 1]` range are allowed, in which
|
||
|
|
case we use `extrapolation_value` to extrapolate the input image values.
|
||
|
|
box_ind: A `Tensor` of type `int32`.
|
||
|
|
A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
|
||
|
|
The value of `box_ind[i]` specifies the image that the `i`-th box refers
|
||
|
|
to.
|
||
|
|
crop_size: A list of two integers `[crop_height, crop_width]`. All
|
||
|
|
cropped image patches are resized to this size. The aspect ratio of the
|
||
|
|
image content is not preserved. Both `crop_height` and `crop_width` need
|
||
|
|
to be positive.
|
||
|
|
num_spatial_bins: A list of two integers `[spatial_bins_y, spatial_bins_x]`.
|
||
|
|
Represents the number of position-sensitive bins in y and x directions.
|
||
|
|
Both values should be >= 1. `crop_height` should be divisible by
|
||
|
|
`spatial_bins_y`, and similarly for width.
|
||
|
|
The number of image channels should be divisible by
|
||
|
|
(spatial_bins_y * spatial_bins_x).
|
||
|
|
Suggested value from R-FCN paper: [3, 3].
|
||
|
|
global_pool: A boolean variable.
|
||
|
|
If True, we perform average global pooling on the features assembled from
|
||
|
|
the position-sensitive score maps.
|
||
|
|
If False, we keep the position-pooled features without global pooling
|
||
|
|
over the spatial coordinates.
|
||
|
|
Note that using global_pool=True is equivalent to but more efficient than
|
||
|
|
running the function with global_pool=False and then performing global
|
||
|
|
average pooling.
|
||
|
|
extrapolation_value: An optional `float`. Defaults to `0`.
|
||
|
|
Value used for extrapolation, when applicable.
|
||
|
|
Returns:
|
||
|
|
position_sensitive_features: A 4-D tensor of shape
|
||
|
|
`[num_boxes, K, K, crop_channels]`,
|
||
|
|
where `crop_channels = depth / (spatial_bins_y * spatial_bins_x)`,
|
||
|
|
where K = 1 when global_pool is True (Average-pooled cropped regions),
|
||
|
|
and K = crop_size when global_pool is False.
|
||
|
|
Raises:
|
||
|
|
ValueError: Raised in four situations:
|
||
|
|
`num_spatial_bins` is not >= 1;
|
||
|
|
`num_spatial_bins` does not divide `crop_size`;
|
||
|
|
`(spatial_bins_y*spatial_bins_x)` does not divide `depth`;
|
||
|
|
`bin_crop_size` is not square when global_pool=False due to the
|
||
|
|
constraint in function space_to_depth.
|
||
|
|
"""
|
||
|
|
total_bins = 1
|
||
|
|
bin_crop_size = []
|
||
|
|
|
||
|
|
for (num_bins, crop_dim) in zip(num_spatial_bins, crop_size):
|
||
|
|
if num_bins < 1:
|
||
|
|
raise ValueError('num_spatial_bins should be >= 1')
|
||
|
|
|
||
|
|
if crop_dim % num_bins != 0:
|
||
|
|
raise ValueError('crop_size should be divisible by num_spatial_bins')
|
||
|
|
|
||
|
|
total_bins *= num_bins
|
||
|
|
bin_crop_size.append(crop_dim // num_bins)
|
||
|
|
|
||
|
|
if not global_pool and bin_crop_size[0] != bin_crop_size[1]:
|
||
|
|
raise ValueError('Only support square bin crop size for now.')
|
||
|
|
|
||
|
|
ymin, xmin, ymax, xmax = tf.unstack(boxes, axis=1)
|
||
|
|
spatial_bins_y, spatial_bins_x = num_spatial_bins
|
||
|
|
|
||
|
|
# Split each box into spatial_bins_y * spatial_bins_x bins.
|
||
|
|
position_sensitive_boxes = []
|
||
|
|
for bin_y in range(spatial_bins_y):
|
||
|
|
step_y = (ymax - ymin) / spatial_bins_y
|
||
|
|
for bin_x in range(spatial_bins_x):
|
||
|
|
step_x = (xmax - xmin) / spatial_bins_x
|
||
|
|
box_coordinates = [ymin + bin_y * step_y,
|
||
|
|
xmin + bin_x * step_x,
|
||
|
|
ymin + (bin_y + 1) * step_y,
|
||
|
|
xmin + (bin_x + 1) * step_x,
|
||
|
|
]
|
||
|
|
position_sensitive_boxes.append(tf.stack(box_coordinates, axis=1))
|
||
|
|
|
||
|
|
image_splits = tf.split(value=image, num_or_size_splits=total_bins, axis=3)
|
||
|
|
|
||
|
|
image_crops = []
|
||
|
|
for (split, box) in zip(image_splits, position_sensitive_boxes):
|
||
|
|
crop = tf.image.crop_and_resize(split, box, box_ind, bin_crop_size,
|
||
|
|
extrapolation_value=extrapolation_value)
|
||
|
|
image_crops.append(crop)
|
||
|
|
|
||
|
|
if global_pool:
|
||
|
|
# Average over all bins.
|
||
|
|
position_sensitive_features = tf.add_n(image_crops) / len(image_crops)
|
||
|
|
# Then average over spatial positions within the bins.
|
||
|
|
position_sensitive_features = tf.reduce_mean(
|
||
|
|
position_sensitive_features, [1, 2], keep_dims=True)
|
||
|
|
else:
|
||
|
|
# Reorder height/width to depth channel.
|
||
|
|
block_size = bin_crop_size[0]
|
||
|
|
if block_size >= 2:
|
||
|
|
image_crops = [tf.space_to_depth(
|
||
|
|
crop, block_size=block_size) for crop in image_crops]
|
||
|
|
|
||
|
|
# Pack image_crops so that first dimension is for position-senstive boxes.
|
||
|
|
position_sensitive_features = tf.stack(image_crops, axis=0)
|
||
|
|
|
||
|
|
# Unroll the position-sensitive boxes to spatial positions.
|
||
|
|
position_sensitive_features = tf.squeeze(
|
||
|
|
tf.batch_to_space_nd(position_sensitive_features,
|
||
|
|
block_shape=[1] + num_spatial_bins,
|
||
|
|
crops=tf.zeros((3, 2), dtype=tf.int32)),
|
||
|
|
squeeze_dims=[0])
|
||
|
|
|
||
|
|
# Reorder back the depth channel.
|
||
|
|
if block_size >= 2:
|
||
|
|
position_sensitive_features = tf.depth_to_space(
|
||
|
|
position_sensitive_features, block_size=block_size)
|
||
|
|
|
||
|
|
return position_sensitive_features
|
||
|
|
|
||
|
|
|
||
|
|
def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
|
||
|
|
image_width):
|
||
|
|
"""Transforms the box masks back to full image masks.
|
||
|
|
|
||
|
|
Embeds masks in bounding boxes of larger masks whose shapes correspond to
|
||
|
|
image shape.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
box_masks: A tf.float32 tensor of size [num_masks, mask_height, mask_width].
|
||
|
|
boxes: A tf.float32 tensor of size [num_masks, 4] containing the box
|
||
|
|
corners. Row i contains [ymin, xmin, ymax, xmax] of the box
|
||
|
|
corresponding to mask i. Note that the box corners are in
|
||
|
|
normalized coordinates.
|
||
|
|
image_height: Image height. The output mask will have the same height as
|
||
|
|
the image height.
|
||
|
|
image_width: Image width. The output mask will have the same width as the
|
||
|
|
image width.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tf.float32 tensor of size [num_masks, image_height, image_width].
|
||
|
|
"""
|
||
|
|
# TODO(rathodv): Make this a public function.
|
||
|
|
def reframe_box_masks_to_image_masks_default():
|
||
|
|
"""The default function when there are more than 0 box masks."""
|
||
|
|
def transform_boxes_relative_to_boxes(boxes, reference_boxes):
|
||
|
|
boxes = tf.reshape(boxes, [-1, 2, 2])
|
||
|
|
min_corner = tf.expand_dims(reference_boxes[:, 0:2], 1)
|
||
|
|
max_corner = tf.expand_dims(reference_boxes[:, 2:4], 1)
|
||
|
|
transformed_boxes = (boxes - min_corner) / (max_corner - min_corner)
|
||
|
|
return tf.reshape(transformed_boxes, [-1, 4])
|
||
|
|
|
||
|
|
box_masks_expanded = tf.expand_dims(box_masks, axis=3)
|
||
|
|
num_boxes = tf.shape(box_masks_expanded)[0]
|
||
|
|
unit_boxes = tf.concat(
|
||
|
|
[tf.zeros([num_boxes, 2]), tf.ones([num_boxes, 2])], axis=1)
|
||
|
|
reverse_boxes = transform_boxes_relative_to_boxes(unit_boxes, boxes)
|
||
|
|
return tf.image.crop_and_resize(
|
||
|
|
image=box_masks_expanded,
|
||
|
|
boxes=reverse_boxes,
|
||
|
|
box_ind=tf.range(num_boxes),
|
||
|
|
crop_size=[image_height, image_width],
|
||
|
|
extrapolation_value=0.0)
|
||
|
|
image_masks = tf.cond(
|
||
|
|
tf.shape(box_masks)[0] > 0,
|
||
|
|
reframe_box_masks_to_image_masks_default,
|
||
|
|
lambda: tf.zeros([0, image_height, image_width, 1], dtype=tf.float32))
|
||
|
|
return tf.squeeze(image_masks, axis=3)
|
||
|
|
|
||
|
|
|
||
|
|
def merge_boxes_with_multiple_labels(boxes, classes, num_classes):
|
||
|
|
"""Merges boxes with same coordinates and returns K-hot encoded classes.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
boxes: A tf.float32 tensor with shape [N, 4] holding N boxes.
|
||
|
|
classes: A tf.int32 tensor with shape [N] holding class indices.
|
||
|
|
The class index starts at 0.
|
||
|
|
num_classes: total number of classes to use for K-hot encoding.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes,
|
||
|
|
where N' <= N.
|
||
|
|
class_encodings: A tf.int32 tensor with shape [N', num_classes] holding
|
||
|
|
k-hot encodings for the merged boxes.
|
||
|
|
merged_box_indices: A tf.int32 tensor with shape [N'] holding original
|
||
|
|
indices of the boxes.
|
||
|
|
"""
|
||
|
|
def merge_numpy_boxes(boxes, classes, num_classes):
|
||
|
|
"""Python function to merge numpy boxes."""
|
||
|
|
if boxes.size < 1:
|
||
|
|
return (np.zeros([0, 4], dtype=np.float32),
|
||
|
|
np.zeros([0, num_classes], dtype=np.int32),
|
||
|
|
np.zeros([0], dtype=np.int32))
|
||
|
|
box_to_class_indices = {}
|
||
|
|
for box_index in range(boxes.shape[0]):
|
||
|
|
box = tuple(boxes[box_index, :].tolist())
|
||
|
|
class_index = classes[box_index]
|
||
|
|
if box not in box_to_class_indices:
|
||
|
|
box_to_class_indices[box] = [box_index, np.zeros([num_classes])]
|
||
|
|
box_to_class_indices[box][1][class_index] = 1
|
||
|
|
merged_boxes = np.vstack(box_to_class_indices.keys()).astype(np.float32)
|
||
|
|
class_encodings = [item[1] for item in box_to_class_indices.values()]
|
||
|
|
class_encodings = np.vstack(class_encodings).astype(np.int32)
|
||
|
|
merged_box_indices = [item[0] for item in box_to_class_indices.values()]
|
||
|
|
merged_box_indices = np.array(merged_box_indices).astype(np.int32)
|
||
|
|
return merged_boxes, class_encodings, merged_box_indices
|
||
|
|
|
||
|
|
merged_boxes, class_encodings, merged_box_indices = tf.py_func(
|
||
|
|
merge_numpy_boxes, [boxes, classes, num_classes],
|
||
|
|
[tf.float32, tf.int32, tf.int32])
|
||
|
|
merged_boxes = tf.reshape(merged_boxes, [-1, 4])
|
||
|
|
class_encodings = tf.reshape(class_encodings, [-1, num_classes])
|
||
|
|
merged_box_indices = tf.reshape(merged_box_indices, [-1])
|
||
|
|
return merged_boxes, class_encodings, merged_box_indices
|
||
|
|
|
||
|
|
|
||
|
|
def nearest_neighbor_upsampling(input_tensor, scale):
|
||
|
|
"""Nearest neighbor upsampling implementation.
|
||
|
|
|
||
|
|
Nearest neighbor upsampling function that maps input tensor with shape
|
||
|
|
[batch_size, height, width, channels] to [batch_size, height * scale
|
||
|
|
, width * scale, channels]. This implementation only uses reshape and
|
||
|
|
broadcasting to make it TPU compatible.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input_tensor: A float32 tensor of size [batch, height_in, width_in,
|
||
|
|
channels].
|
||
|
|
scale: An integer multiple to scale resolution of input data.
|
||
|
|
Returns:
|
||
|
|
data_up: A float32 tensor of size
|
||
|
|
[batch, height_in*scale, width_in*scale, channels].
|
||
|
|
"""
|
||
|
|
with tf.name_scope('nearest_neighbor_upsampling'):
|
||
|
|
(batch_size, height, width,
|
||
|
|
channels) = shape_utils.combined_static_and_dynamic_shape(input_tensor)
|
||
|
|
output_tensor = tf.reshape(
|
||
|
|
input_tensor, [batch_size, height, 1, width, 1, channels]) * tf.ones(
|
||
|
|
[1, 1, scale, 1, scale, 1], dtype=input_tensor.dtype)
|
||
|
|
return tf.reshape(output_tensor,
|
||
|
|
[batch_size, height * scale, width * scale, channels])
|
||
|
|
|
||
|
|
|
||
|
|
def matmul_gather_on_zeroth_axis(params, indices, scope=None):
|
||
|
|
"""Matrix multiplication based implementation of tf.gather on zeroth axis.
|
||
|
|
|
||
|
|
TODO(rathodv, jonathanhuang): enable sparse matmul option.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
params: A float32 Tensor. The tensor from which to gather values.
|
||
|
|
Must be at least rank 1.
|
||
|
|
indices: A Tensor. Must be one of the following types: int32, int64.
|
||
|
|
Must be in range [0, params.shape[0])
|
||
|
|
scope: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A Tensor. Has the same type as params. Values from params gathered
|
||
|
|
from indices given by indices, with shape indices.shape + params.shape[1:].
|
||
|
|
"""
|
||
|
|
with tf.name_scope(scope, 'MatMulGather'):
|
||
|
|
params_shape = shape_utils.combined_static_and_dynamic_shape(params)
|
||
|
|
indices_shape = shape_utils.combined_static_and_dynamic_shape(indices)
|
||
|
|
params2d = tf.reshape(params, [params_shape[0], -1])
|
||
|
|
indicator_matrix = tf.one_hot(indices, params_shape[0])
|
||
|
|
gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
|
||
|
|
return tf.reshape(gathered_result_flattened,
|
||
|
|
tf.stack(indices_shape + params_shape[1:]))
|
||
|
|
|
||
|
|
|
||
|
|
def matmul_crop_and_resize(image, boxes, crop_size, scope=None):
|
||
|
|
"""Matrix multiplication based implementation of the crop and resize op.
|
||
|
|
|
||
|
|
Extracts crops from the input image tensor and bilinearly resizes them
|
||
|
|
(possibly with aspect ratio change) to a common output size specified by
|
||
|
|
crop_size. This is more general than the crop_to_bounding_box op which
|
||
|
|
extracts a fixed size slice from the input image and does not allow
|
||
|
|
resizing or aspect ratio change.
|
||
|
|
|
||
|
|
Returns a tensor with crops from the input image at positions defined at
|
||
|
|
the bounding box locations in boxes. The cropped boxes are all resized
|
||
|
|
(with bilinear interpolation) to a fixed size = `[crop_height, crop_width]`.
|
||
|
|
The result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`.
|
||
|
|
|
||
|
|
Running time complexity:
|
||
|
|
O((# channels) * (# boxes) * (crop_size)^2 * M), where M is the number
|
||
|
|
of pixels of the longer edge of the image.
|
||
|
|
|
||
|
|
Note that this operation is meant to replicate the behavior of the standard
|
||
|
|
tf.image.crop_and_resize operation but there are a few differences.
|
||
|
|
Specifically:
|
||
|
|
1) The extrapolation value (the values that are interpolated from outside
|
||
|
|
the bounds of the image window) is always zero
|
||
|
|
2) Only XLA supported operations are used (e.g., matrix multiplication).
|
||
|
|
3) There is no `box_indices` argument --- to run this op on multiple images,
|
||
|
|
one must currently call this op independently on each image.
|
||
|
|
4) All shapes and the `crop_size` parameter are assumed to be statically
|
||
|
|
defined. Moreover, the number of boxes must be strictly nonzero.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
image: A `Tensor`. Must be one of the following types: `uint8`, `int8`,
|
||
|
|
`int16`, `int32`, `int64`, `half`, `float32`, `float64`.
|
||
|
|
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
|
||
|
|
Both `image_height` and `image_width` need to be positive.
|
||
|
|
boxes: A `Tensor` of type `float32`.
|
||
|
|
A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
|
||
|
|
specifies the coordinates of a box in the `box_ind[i]` image and is
|
||
|
|
specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized
|
||
|
|
coordinate value of `y` is mapped to the image coordinate at
|
||
|
|
`y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image
|
||
|
|
height is mapped to `[0, image_height - 1] in image height coordinates.
|
||
|
|
We do allow y1 > y2, in which case the sampled crop is an up-down flipped
|
||
|
|
version of the original image. The width dimension is treated similarly.
|
||
|
|
Normalized coordinates outside the `[0, 1]` range are allowed, in which
|
||
|
|
case we use `extrapolation_value` to extrapolate the input image values.
|
||
|
|
crop_size: A list of two integers `[crop_height, crop_width]`. All
|
||
|
|
cropped image patches are resized to this size. The aspect ratio of the
|
||
|
|
image content is not preserved. Both `crop_height` and `crop_width` need
|
||
|
|
to be positive.
|
||
|
|
scope: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: if image tensor does not have shape
|
||
|
|
`[1, image_height, image_width, depth]` and all dimensions statically
|
||
|
|
defined.
|
||
|
|
ValueError: if boxes tensor does not have shape `[num_boxes, 4]` where
|
||
|
|
num_boxes > 0.
|
||
|
|
ValueError: if crop_size is not a list of two positive integers
|
||
|
|
"""
|
||
|
|
img_shape = image.shape.as_list()
|
||
|
|
boxes_shape = boxes.shape.as_list()
|
||
|
|
_, img_height, img_width, _ = img_shape
|
||
|
|
if not isinstance(crop_size, list) or len(crop_size) != 2:
|
||
|
|
raise ValueError('`crop_size` must be a list of length 2')
|
||
|
|
dimensions = img_shape + crop_size + boxes_shape
|
||
|
|
if not all([isinstance(dim, int) for dim in dimensions]):
|
||
|
|
raise ValueError('all input shapes must be statically defined')
|
||
|
|
if len(crop_size) != 2:
|
||
|
|
raise ValueError('`crop_size` must be a list of length 2')
|
||
|
|
if len(boxes_shape) != 2 or boxes_shape[1] != 4:
|
||
|
|
raise ValueError('`boxes` should have shape `[num_boxes, 4]`')
|
||
|
|
if len(img_shape) != 4 and img_shape[0] != 1:
|
||
|
|
raise ValueError('image should have shape '
|
||
|
|
'`[1, image_height, image_width, depth]`')
|
||
|
|
num_crops = boxes_shape[0]
|
||
|
|
if not num_crops > 0:
|
||
|
|
raise ValueError('number of boxes must be > 0')
|
||
|
|
if not (crop_size[0] > 0 and crop_size[1] > 0):
|
||
|
|
raise ValueError('`crop_size` must be a list of two positive integers.')
|
||
|
|
|
||
|
|
def _lin_space_weights(num, img_size):
|
||
|
|
if num > 1:
|
||
|
|
alpha = (img_size - 1) / float(num - 1)
|
||
|
|
indices = np.reshape(np.arange(num), (1, num))
|
||
|
|
start_weights = alpha * (num - 1 - indices)
|
||
|
|
stop_weights = alpha * indices
|
||
|
|
else:
|
||
|
|
start_weights = num * [.5 * (img_size - 1)]
|
||
|
|
stop_weights = num * [.5 * (img_size - 1)]
|
||
|
|
return (tf.constant(start_weights, dtype=tf.float32),
|
||
|
|
tf.constant(stop_weights, dtype=tf.float32))
|
||
|
|
|
||
|
|
with tf.name_scope(scope, 'MatMulCropAndResize'):
|
||
|
|
y1_weights, y2_weights = _lin_space_weights(crop_size[0], img_height)
|
||
|
|
x1_weights, x2_weights = _lin_space_weights(crop_size[1], img_width)
|
||
|
|
[y1, x1, y2, x2] = tf.split(value=boxes, num_or_size_splits=4, axis=1)
|
||
|
|
|
||
|
|
# Pixel centers of input image and grid points along height and width
|
||
|
|
image_idx_h = tf.constant(
|
||
|
|
np.reshape(np.arange(img_height), (1, 1, img_height)), dtype=tf.float32)
|
||
|
|
image_idx_w = tf.constant(
|
||
|
|
np.reshape(np.arange(img_width), (1, 1, img_width)), dtype=tf.float32)
|
||
|
|
grid_pos_h = tf.expand_dims(y1 * y1_weights + y2 * y2_weights, 2)
|
||
|
|
grid_pos_w = tf.expand_dims(x1 * x1_weights + x2 * x2_weights, 2)
|
||
|
|
|
||
|
|
# Create kernel matrices of pairwise kernel evaluations between pixel
|
||
|
|
# centers of image and grid points.
|
||
|
|
kernel_h = tf.nn.relu(1 - tf.abs(image_idx_h - grid_pos_h))
|
||
|
|
kernel_w = tf.nn.relu(1 - tf.abs(image_idx_w - grid_pos_w))
|
||
|
|
|
||
|
|
# TODO(jonathanhuang): investigate whether all channels can be processed
|
||
|
|
# without the explicit unstack --- possibly with a permute and map_fn call.
|
||
|
|
result_channels = []
|
||
|
|
for channel in tf.unstack(image, axis=3):
|
||
|
|
result_channels.append(
|
||
|
|
tf.matmul(
|
||
|
|
tf.matmul(kernel_h, tf.tile(channel, [num_crops, 1, 1])),
|
||
|
|
kernel_w, transpose_b=True))
|
||
|
|
return tf.stack(result_channels, axis=3)
|