Source code for tensorlayer.layers.convolution.deformable_conv
#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.core import LayersConfig
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.decorators import private_method
__all__ = [
'DeformableConv2d',
]
[docs]class DeformableConv2d(Layer):
"""The :class:`DeformableConv2d` class is a 2D
`Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`__.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer.
offset_layer : :class:`Layer`
To predict the offset of convolution operations.
The output shape is (batchsize, input height, input width, 2*(number of element in the convolution kernel))
e.g. if apply a 3*3 kernel, the number of the last dimension should be 18 (2*3*3)
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size (height, width).
act : activation function
The activation function of this layer.
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer.
b_init_args : dictionary
The arguments for the bias vector initializer.
name : str
A unique layer name.
Examples
--------
>>> net = tl.layers.InputLayer(x, name='input_layer')
>>> offset1 = tl.layers.Conv2d(net, 18, (3, 3), (1, 1), act=act, padding='SAME', name='offset1')
>>> net = tl.layers.DeformableConv2d(net, offset1, 32, (3, 3), act=act, name='deformable1')
>>> offset2 = tl.layers.Conv2d(net, 18, (3, 3), (1, 1), act=act, padding='SAME', name='offset2')
>>> net = tl.layers.DeformableConv2d(net, offset2, 64, (3, 3), act=act, name='deformable2')
References
----------
- The deformation operation was adapted from the implementation in `here <https://github.com/felixlaumon/deform-conv>`__
Notes
-----
- The padding is fixed to 'SAME'.
- The current implementation is not optimized for memory usgae. Please use it carefully.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
offset_layer=None,
# shape=(3, 3, 1, 100),
n_filter=32,
filter_size=(3, 3),
act=None,
name='deformable_conv_2d',
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None,
b_init_args=None
):
super(DeformableConv2d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"DeformableConv2d %s: n_filter: %d, filter_size: %s act: %s" %
(self.name, n_filter, str(filter_size), self.act.__name__ if self.act is not None else 'No Activation')
)
self.offset_layer = offset_layer
try:
pre_channel = int(prev_layer.outputs.get_shape()[-1])
except Exception: # if pre_channel is ?, it happens when using Spatial Transformer Net
pre_channel = 1
logging.info("[warnings] unknow input channels, set to 1")
shape = (filter_size[0], filter_size[1], pre_channel, n_filter)
with tf.variable_scope(name):
offset = self.offset_layer.outputs
if offset.get_shape()[-1] != 2 * shape[0] * shape[1]:
raise AssertionError("offset.get_shape()[-1] is not equal to: %d" % 2 * shape[0] * shape[1])
# Grid initialisation
input_h = int(self.inputs.get_shape()[1])
input_w = int(self.inputs.get_shape()[2])
kernel_n = shape[0] * shape[1]
initial_offsets = tf.stack(tf.meshgrid(tf.range(shape[0]), tf.range(shape[1]),
indexing='ij')) # initial_offsets --> (kh, kw, 2)
initial_offsets = tf.reshape(initial_offsets, (-1, 2)) # initial_offsets --> (n, 2)
initial_offsets = tf.expand_dims(initial_offsets, 0) # initial_offsets --> (1, n, 2)
initial_offsets = tf.expand_dims(initial_offsets, 0) # initial_offsets --> (1, 1, n, 2)
initial_offsets = tf.tile(initial_offsets, [input_h, input_w, 1, 1]) # initial_offsets --> (h, w, n, 2)
initial_offsets = tf.cast(initial_offsets, 'float32')
grid = tf.meshgrid(
tf.range(-int((shape[0] - 1) / 2.0), int(input_h - int((shape[0] - 1) / 2.0)), 1),
tf.range(-int((shape[1] - 1) / 2.0), int(input_w - int((shape[1] - 1) / 2.0)), 1), indexing='ij'
)
grid = tf.stack(grid, axis=-1)
grid = tf.cast(grid, 'float32') # grid --> (h, w, 2)
grid = tf.expand_dims(grid, 2) # grid --> (h, w, 1, 2)
grid = tf.tile(grid, [1, 1, kernel_n, 1]) # grid --> (h, w, n, 2)
grid_offset = grid + initial_offsets # grid_offset --> (h, w, n, 2)
input_deform = self._tf_batch_map_offsets(self.inputs, offset, grid_offset)
W = tf.get_variable(
name='W_deformableconv2d', shape=[1, 1, shape[0] * shape[1], shape[-2], shape[-1]], initializer=W_init,
dtype=LayersConfig.tf_dtype, **self.W_init_args
)
_tensor = tf.nn.conv3d(input_deform, W, strides=[1, 1, 1, 1, 1], padding='VALID', name=None)
if b_init:
b = tf.get_variable(
name='b_deformableconv2d', shape=(shape[-1]), initializer=b_init, dtype=LayersConfig.tf_dtype,
**self.b_init_args
)
_tensor = tf.nn.bias_add(_tensor, b, name='bias_add')
self.outputs = tf.reshape(
tensor=self._apply_activation(_tensor), shape=[tf.shape(self.inputs)[0], input_h, input_w, shape[-1]]
)
self._add_layers(self.outputs)
if b_init:
self._add_params([W, b])
else:
self._add_params(W)
@private_method
def _to_bc_h_w(self, x, x_shape):
"""(b, h, w, c) -> (b*c, h, w)"""
x = tf.transpose(x, [0, 3, 1, 2])
x = tf.reshape(x, (-1, x_shape[1], x_shape[2]))
return x
@private_method
def _to_b_h_w_n_c(self, x, x_shape):
"""(b*c, h, w, n) -> (b, h, w, n, c)"""
x = tf.reshape(x, (-1, x_shape[4], x_shape[1], x_shape[2], x_shape[3]))
x = tf.transpose(x, [0, 2, 3, 4, 1])
return x
@private_method
def tf_flatten(self, a):
"""Flatten tensor"""
return tf.reshape(a, [-1])
@private_method
def _get_vals_by_coords(self, inputs, coords, idx, out_shape):
indices = tf.stack(
[idx, self.tf_flatten(coords[:, :, :, :, 0]),
self.tf_flatten(coords[:, :, :, :, 1])], axis=-1
)
vals = tf.gather_nd(inputs, indices)
vals = tf.reshape(vals, out_shape)
return vals
@private_method
def _tf_repeat(self, a, repeats):
"""Tensorflow version of np.repeat for 1D"""
# https://github.com/tensorflow/tensorflow/issues/8521
if len(a.get_shape()) != 1:
raise AssertionError("This is not a 1D Tensor")
a = tf.expand_dims(a, -1)
a = tf.tile(a, [1, repeats])
a = self.tf_flatten(a)
return a
@private_method
def _tf_batch_map_coordinates(self, inputs, coords):
"""Batch version of tf_map_coordinates
Only supports 2D feature maps
Parameters
----------
inputs : ``tf.Tensor``
shape = (b*c, h, w)
coords : ``tf.Tensor``
shape = (b*c, h, w, n, 2)
Returns
-------
``tf.Tensor``
A Tensor with the shape as (b*c, h, w, n)
"""
input_shape = inputs.get_shape()
coords_shape = coords.get_shape()
batch_channel = tf.shape(inputs)[0]
input_h = int(input_shape[1])
input_w = int(input_shape[2])
kernel_n = int(coords_shape[3])
n_coords = input_h * input_w * kernel_n
coords_lt = tf.cast(tf.floor(coords), 'int32')
coords_rb = tf.cast(tf.ceil(coords), 'int32')
coords_lb = tf.stack([coords_lt[:, :, :, :, 0], coords_rb[:, :, :, :, 1]], axis=-1)
coords_rt = tf.stack([coords_rb[:, :, :, :, 0], coords_lt[:, :, :, :, 1]], axis=-1)
idx = self._tf_repeat(tf.range(batch_channel), n_coords)
vals_lt = self._get_vals_by_coords(inputs, coords_lt, idx, (batch_channel, input_h, input_w, kernel_n))
vals_rb = self._get_vals_by_coords(inputs, coords_rb, idx, (batch_channel, input_h, input_w, kernel_n))
vals_lb = self._get_vals_by_coords(inputs, coords_lb, idx, (batch_channel, input_h, input_w, kernel_n))
vals_rt = self._get_vals_by_coords(inputs, coords_rt, idx, (batch_channel, input_h, input_w, kernel_n))
coords_offset_lt = coords - tf.cast(coords_lt, 'float32')
vals_t = vals_lt + (vals_rt - vals_lt) * coords_offset_lt[:, :, :, :, 0]
vals_b = vals_lb + (vals_rb - vals_lb) * coords_offset_lt[:, :, :, :, 0]
mapped_vals = vals_t + (vals_b - vals_t) * coords_offset_lt[:, :, :, :, 1]
return mapped_vals
@private_method
def _tf_batch_map_offsets(self, inputs, offsets, grid_offset):
"""Batch map offsets into input
Parameters
------------
inputs : ``tf.Tensor``
shape = (b, h, w, c)
offsets: ``tf.Tensor``
shape = (b, h, w, 2*n)
grid_offset: `tf.Tensor``
Offset grids shape = (h, w, n, 2)
Returns
-------
``tf.Tensor``
A Tensor with the shape as (b, h, w, c)
"""
input_shape = inputs.get_shape()
batch_size = tf.shape(inputs)[0]
kernel_n = int(int(offsets.get_shape()[3]) / 2)
input_h = input_shape[1]
input_w = input_shape[2]
channel = input_shape[3]
# inputs (b, h, w, c) --> (b*c, h, w)
inputs = self._to_bc_h_w(inputs, input_shape)
# offsets (b, h, w, 2*n) --> (b, h, w, n, 2)
offsets = tf.reshape(offsets, (batch_size, input_h, input_w, kernel_n, 2))
# offsets (b, h, w, n, 2) --> (b*c, h, w, n, 2)
# offsets = tf.tile(offsets, [channel, 1, 1, 1, 1])
coords = tf.expand_dims(grid_offset, 0) # grid_offset --> (1, h, w, n, 2)
coords = tf.tile(coords, [batch_size, 1, 1, 1, 1]) + offsets # grid_offset --> (b, h, w, n, 2)
# clip out of bound
coords = tf.stack(
[
tf.clip_by_value(coords[:, :, :, :, 0], 0.0, tf.cast(input_h - 1, 'float32')),
tf.clip_by_value(coords[:, :, :, :, 1], 0.0, tf.cast(input_w - 1, 'float32'))
], axis=-1
)
coords = tf.tile(coords, [channel, 1, 1, 1, 1])
mapped_vals = self._tf_batch_map_coordinates(inputs, coords)
# (b*c, h, w, n) --> (b, h, w, n, c)
mapped_vals = self._to_b_h_w_n_c(mapped_vals, [batch_size, input_h, input_w, kernel_n, channel])
return mapped_vals