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Port backends to Keras-2 API (#44)

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* Backport tensorflow backend

* Backport theano backend

* Fixed import issues

* Fixed tests

* Fixed theano backend
This commit is contained in:
Abhai Kollara Dilip
2017-03-19 03:42:39 +05:30
committed by Michael Oliver
parent dcfe9ebac3
commit 88f4e4f3d8
5 changed files with 63 additions and 58 deletions
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keras_contrib/backend/tensorflow_backend.py
+28 -27
View File
@@ -11,10 +11,10 @@ from keras.backend import tensorflow_backend as KTF
import numpy as np
import os
import warnings
from keras.backend.common import floatx, _EPSILON, image_data_format, reset_uids
from keras.backend.common import floatx, _EPSILON, image_data_format
from keras.backend.tensorflow_backend import reset_uids
from keras.backend.tensorflow_backend import _preprocess_conv3d_input
from keras.backend.tensorflow_backend import _preprocess_conv3d_kernel
from keras.backend.tensorflow_backend import _preprocess_border_mode
from keras.backend.tensorflow_backend import _postprocess_conv3d_output
from keras.backend.tensorflow_backend import _preprocess_padding
from keras.backend.tensorflow_backend import _preprocess_conv2d_input
@@ -23,8 +23,8 @@ from keras.backend.tensorflow_backend import _postprocess_conv2d_output
py_all = all
def _preprocess_deconv_output_shape(x, shape, dim_ordering):
if dim_ordering == 'th':
def _preprocess_deconv_output_shape(x, shape, data_format):
if data_format == 'channels_first':
shape = (shape[0],) + tuple(shape[2:]) + (shape[1],)
if shape[0] is None:
@@ -34,8 +34,8 @@ def _preprocess_deconv_output_shape(x, shape, dim_ordering):
def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
border_mode='valid',
dim_ordering='default',
padding='valid',
data_format='default',
image_shape=None, filter_shape=None):
'''3D deconvolution (i.e. transposed convolution).
@@ -44,8 +44,8 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
kernel: kernel tensor.
output_shape: 1D int tensor for the output shape.
strides: strides tuple.
border_mode: string, "same" or "valid".
dim_ordering: "tf" or "th".
padding: string, "same" or "valid".
data_format: "channels_last" or "channels_first".
Whether to use Theano or TensorFlow dimension ordering
for inputs/kernels/ouputs.
@@ -53,28 +53,29 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
A tensor, result of transposed 3D convolution.
# Raises
ValueError: if `dim_ordering` is neither `tf` or `th`.
ValueError: if `image_data_format` is neither
`channels_last` or `channels_first`.
'''
if dim_ordering == 'default':
dim_ordering = image_dim_ordering()
if dim_ordering not in {'th', 'tf'}:
raise ValueError('Unknown dim_ordering ' + str(dim_ordering))
if data_format == 'default':
data_format = image_data_format()
if data_format not in {'channels_last', 'channels_first'}:
raise ValueError('Unknown image data format ' + str(data_format))
x = _preprocess_conv3d_input(x, dim_ordering)
x = _preprocess_conv3d_input(x, data_format)
output_shape = _preprocess_deconv_output_shape(x, output_shape,
dim_ordering)
kernel = _preprocess_conv3d_kernel(kernel, dim_ordering)
data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
kernel = tf.transpose(kernel, (0, 1, 2, 4, 3))
padding = _preprocess_border_mode(border_mode)
padding = _preprocess_padding(padding)
strides = (1,) + strides + (1,)
x = tf.nn.conv3d_transpose(x, kernel, output_shape, strides,
padding=padding)
return _postprocess_conv3d_output(x, dim_ordering)
return _postprocess_conv3d_output(x, data_format)
def extract_image_patches(x, ksizes, ssizes, border_mode="same",
dim_ordering="tf"):
def extract_image_patches(x, ksizes, ssizes, padding="same",
data_format="channels_last"):
'''
Extract the patches from an image
# Parameters
@@ -82,8 +83,8 @@ def extract_image_patches(x, ksizes, ssizes, border_mode="same",
x : The input image
ksizes : 2-d tuple with the kernel size
ssizes : 2-d tuple with the strides size
border_mode : 'same' or 'valid'
dim_ordering : 'tf' or 'th'
padding : 'same' or 'valid'
data_format : 'channels_last' or 'channels_first'
# Returns
The (k_w,k_h) patches extracted
@@ -92,8 +93,8 @@ def extract_image_patches(x, ksizes, ssizes, border_mode="same",
'''
kernel = [1, ksizes[0], ksizes[1], 1]
strides = [1, ssizes[0], ssizes[1], 1]
padding = _preprocess_border_mode(border_mode)
if dim_ordering == "th":
padding = _preprocess_padding(padding)
if data_format == "channels_first":
x = KTF.permute_dimensions(x, (0, 2, 3, 1))
bs_i, w_i, h_i, ch_i = KTF.int_shape(x)
patches = tf.extract_image_patches(x, kernel, strides, [1, 1, 1, 1],
@@ -103,7 +104,7 @@ def extract_image_patches(x, ksizes, ssizes, border_mode="same",
patches = tf.reshape(patches, [bs, w, h, -1, ch_i])
patches = tf.reshape(tf.transpose(patches, [0, 1, 2, 4, 3]),
[bs, w, h, ch_i, ksizes[0], ksizes[1]])
if dim_ordering == "tf":
if data_format == "channels_last":
patches = KTF.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])
return patches
@@ -111,9 +112,9 @@ def extract_image_patches(x, ksizes, ssizes, border_mode="same",
def depth_to_space(input, scale):
''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
input = _preprocess_conv2d_input(input, image_dim_ordering())
input = _preprocess_conv2d_input(input, image_data_format())
out = tf.depth_to_space(input, scale)
out = _postprocess_conv2d_output(out, image_dim_ordering())
out = _postprocess_conv2d_output(out, image_data_format())
return out
keras_contrib/backend/theano_backend.py
+26 -26
View File
@@ -33,8 +33,8 @@ py_all = all
def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
border_mode='valid',
dim_ordering='default',
padding='valid',
data_format='default',
image_shape=None, filter_shape=None):
'''3D deconvolution (transposed convolution).
@@ -42,25 +42,25 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
kernel: kernel tensor.
output_shape: desired dimensions of output.
strides: strides tuple.
border_mode: string, "same" or "valid".
dim_ordering: "tf" or "th".
padding: string, "same" or "valid".
data_format: "channels_last" or "channels_first".
Whether to use Theano or TensorFlow dimension ordering
in inputs/kernels/ouputs.
'''
flip_filters = False
if dim_ordering == 'default':
dim_ordering = image_dim_ordering()
if dim_ordering not in {'th', 'tf'}:
raise ValueError('Unknown dim_ordering ' + str(dim_ordering))
if data_format == 'default':
data_format = image_data_format()
if data_format not in {'channels_last', 'channels_first'}:
raise ValueError('Unknown image data format ' + str(data_format))
if dim_ordering == 'tf':
if data_format == 'channels_last':
output_shape = (output_shape[0], output_shape[4], output_shape[1],
output_shape[2], output_shape[3])
x = _preprocess_conv3d_input(x, dim_ordering)
kernel = _preprocess_conv3d_kernel(kernel, dim_ordering)
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
kernel = kernel.dimshuffle((1, 0, 2, 3, 4))
th_border_mode = _preprocess_border_mode(border_mode)
th_padding = _preprocess_padding(padding)
if hasattr(kernel, '_keras_shape'):
kernel_shape = kernel._keras_shape
@@ -68,22 +68,22 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
# Will only work if `kernel` is a shared variable.
kernel_shape = kernel.eval().shape
filter_shape = _preprocess_conv3d_filter_shape(dim_ordering, filter_shape)
filter_shape = _preprocess_conv3d_filter_shape(data_format, filter_shape)
filter_shape = tuple(filter_shape[i] for i in (1, 0, 2, 3, 4))
conv_out = T.nnet.abstract_conv.conv3d_grad_wrt_inputs(
x, kernel, output_shape,
filter_shape=filter_shape,
border_mode=th_border_mode,
border_mode=th_padding,
subsample=strides,
filter_flip=not flip_filters)
conv_out = _postprocess_conv3d_output(conv_out, x, border_mode,
kernel_shape, strides, dim_ordering)
conv_out = _postprocess_conv3d_output(conv_out, x, padding,
kernel_shape, strides, data_format)
return conv_out
def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering="th"):
def extract_image_patches(X, ksizes, strides, padding="valid", data_format="channels_first"):
'''
Extract the patches from an image
Parameters
@@ -91,8 +91,8 @@ def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering=
X : The input image
ksizes : 2-d tuple with the kernel size
strides : 2-d tuple with the strides size
border_mode : 'same' or 'valid'
dim_ordering : 'tf' or 'th'
padding : 'same' or 'valid'
data_format : 'channels_last' or 'channels_first'
Returns
-------
The (k_w,k_h) patches extracted
@@ -100,9 +100,9 @@ def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering=
TH ==> (batch_size,w,h,c,k_w,k_h)
'''
patch_size = ksizes[1]
if border_mode == "same":
border_mode = "ignore_borders"
if dim_ordering == "tf":
if padding == "same":
padding = "ignore_borders"
if data_format == "channels_last":
X = KTH.permute_dimensions(X, [0, 3, 1, 2])
# Thanks to https://github.com/awentzonline for the help!
batch, c, w, h = KTH.shape(X)
@@ -110,13 +110,13 @@ def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering=
num_rows = 1 + (xs[-2] - patch_size) // strides[1]
num_cols = 1 + (xs[-1] - patch_size) // strides[1]
num_channels = xs[-3]
patches = images2neibs(X, ksizes, strides, border_mode)
patches = images2neibs(X, ksizes, strides, padding)
# Theano is sorting by channel
patches = KTH.reshape(patches, (batch, num_channels, KTH.shape(patches)[0] // num_channels, patch_size, patch_size))
patches = KTH.permute_dimensions(patches, (0, 2, 1, 3, 4))
# arrange in a 2d-grid (rows, cols, channels, px, py)
patches = KTH.reshape(patches, (batch, num_rows, num_cols, num_channels, patch_size, patch_size))
if dim_ordering == "tf":
if data_format == "channels_last":
patches = KTH.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])
return patches
@@ -124,7 +124,7 @@ def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering=
def depth_to_space(input, scale):
''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
input = _preprocess_conv2d_input(input, image_dim_ordering())
input = _preprocess_conv2d_input(input, image_data_format())
b, k, row, col = input.shape
output_shape = (b, k // (scale ** 2), row * scale, col * scale)
@@ -135,7 +135,7 @@ def depth_to_space(input, scale):
for y, x in itertools.product(range(scale), repeat=2):
out = T.inc_subtensor(out[:, :, y::r, x::r], input[:, r * y + x:: r * r, :, :])
out = _postprocess_conv2d_output(out, input, None, None, None, image_dim_ordering())
out = _postprocess_conv2d_output(out, input, None, None, None, image_data_format())
return out
keras_contrib/initializers.py
+4
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@@ -0,0 +1,4 @@
from __future__ import absolute_import
from . import backend as K
from keras.initializers import *
keras_contrib/losses.py
View File
tests/keras_contrib/backend/test_backends.py
+5 -5
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@@ -9,7 +9,7 @@ from keras.backend import tensorflow_backend as KTF
from keras_contrib import backend as KC
import keras_contrib.backend.theano_backend as KCTH
import keras_contrib.backend.tensorflow_backend as KCTF
from keras.utils.np_utils import convert_kernel
from keras.utils.conv_utils import convert_kernel
def check_dtype(var, dtype):
@@ -82,8 +82,8 @@ class TestBackend(object):
strides = [kernel_shape, kernel_shape]
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
ztf = KTF.eval(KCTF.extract_image_patches(xtf, kernel, strides, dim_ordering='th', border_mode="valid"))
zth = KTH.eval(KCTH.extract_image_patches(xth, kernel, strides, dim_ordering='th', border_mode="valid"))
ztf = KTF.eval(KCTF.extract_image_patches(xtf, kernel, strides, data_format='channels_first', padding="valid"))
zth = KTH.eval(KCTH.extract_image_patches(xth, kernel, strides, data_format='channels_first', padding="valid"))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-02)
@@ -95,8 +95,8 @@ class TestBackend(object):
strides = [kernel_shape, kernel_shape]
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
ztf = KTF.eval(KCTF.extract_image_patches(xtf, kernel, strides, dim_ordering='tf', border_mode="same"))
zth = KTH.eval(KCTH.extract_image_patches(xth, kernel, strides, dim_ordering='tf', border_mode="same"))
ztf = KTF.eval(KCTF.extract_image_patches(xtf, kernel, strides, data_format='channels_last', padding="same"))
zth = KTH.eval(KCTH.extract_image_patches(xth, kernel, strides, data_format='channels_last', padding="same"))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-02)