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update backends to K2

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farizrahman4u
2017-03-19 07:24:25 +05:30
parent 15bc52cd6d
commit aebac0335a
3 changed files with 151 additions and 41 deletions
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keras_contrib/backend/tensorflow_backend.py
+68 -21
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@@ -12,20 +12,20 @@ from keras.backend import tensorflow_backend as KTF
import numpy as np
import os
import warnings
from keras.backend.common import floatx, _EPSILON, image_dim_ordering, reset_uids
from keras.backend.common import floatx, _EPSILON, image_data_format, 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_padding
from keras.backend.tensorflow_backend import _postprocess_conv3d_output
from keras.backend.tensorflow_backend import _preprocess_border_mode
from keras.backend.tensorflow_backend import _preprocess_padding
from keras.backend.tensorflow_backend import _preprocess_conv2d_input
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:
@@ -34,9 +34,56 @@ def _preprocess_deconv_output_shape(x, shape, dim_ordering):
return shape
def conv2d(x, kernel, strides=(1, 1), padding='valid', data_format='channels_first',
image_shape=None, filter_shape=None):
'''2D convolution.
# Arguments
kernel: kernel tensor.
strides: strides tuple.
padding: string, "same" or "valid".
data_format: "tf" or "th". Whether to use Theano or TensorFlow dimension ordering
in inputs/kernels/ouputs.
'''
if padding == 'same':
padding = 'SAME'
elif padding == 'valid':
padding = 'VALID'
else:
raise Exception('Invalid border mode: ' + str(padding))
strides = (1,) + strides + (1,)
if _FLOATX == 'float64':
# tf conv2d only supports float32
x = tf.cast(x, 'float32')
kernel = tf.cast(kernel, 'float32')
if data_format == 'channels_first':
# TF uses the last dimension as channel dimension,
# instead of the 2nd one.
# TH input shape: (samples, input_depth, rows, cols)
# TF input shape: (samples, rows, cols, input_depth)
# TH kernel shape: (depth, input_depth, rows, cols)
# TF kernel shape: (rows, cols, input_depth, depth)
x = tf.transpose(x, (0, 2, 3, 1))
kernel = tf.transpose(kernel, (2, 3, 1, 0))
x = tf.nn.conv2d(x, kernel, strides, padding=padding)
x = tf.transpose(x, (0, 3, 1, 2))
elif data_format == 'channels_last':
x = tf.nn.conv2d(x, kernel, strides, padding=padding)
else:
raise Exception('Unknown data_format: ' + str(data_format))
if _FLOATX == 'float64':
x = tf.cast(x, 'float64')
return x
def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
padding='valid',
dim_ordering='default',
data_format='default',
image_shape=None, filter_shape=None):
'''3D deconvolution (i.e. transposed convolution).
@@ -46,7 +93,7 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
output_shape: 1D int tensor for the output shape.
strides: strides tuple.
padding: string, "same" or "valid".
dim_ordering: "tf" or "th".
data_format: "tf" or "th".
Whether to use Theano or TensorFlow dimension ordering
for inputs/kernels/ouputs.
@@ -54,28 +101,28 @@ 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 `data_format` is neither `tf` or `th`.
'''
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_first', 'channels_last'}:
raise ValueError('Unknown 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_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, padding="same",
dim_ordering="tf"):
data_format="tf"):
'''
Extract the patches from an image
# Parameters
@@ -84,7 +131,7 @@ def extract_image_patches(x, ksizes, ssizes, padding="same",
ksizes : 2-d tuple with the kernel size
ssizes : 2-d tuple with the strides size
padding : 'same' or 'valid'
dim_ordering : 'tf' or 'th'
data_format : 'channels_last' or 'channels_first'
# Returns
The (k_w,k_h) patches extracted
@@ -94,7 +141,7 @@ def extract_image_patches(x, ksizes, ssizes, padding="same",
kernel = [1, ksizes[0], ksizes[1], 1]
strides = [1, ssizes[0], ssizes[1], 1]
padding = _preprocess_padding(padding)
if dim_ordering == "th":
if data_format == "th":
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 +150,7 @@ def extract_image_patches(x, ksizes, ssizes, padding="same",
bs, w, h, ch = KTF.int_shape(patches)
patches = tf.reshape(tf.transpose(tf.reshape(patches, [-1, w, h, tf.floordiv(ch, ch_i), ch_i]), [0, 1, 2, 4, 3]),
[-1, w, h, ch_i, ksizes[0], ksizes[1]])
if dim_ordering == "tf":
if data_format == "tf":
patches = KTF.permute_dimensions(patches, [0, 1, 2, 4, 5, 3])
return patches
@@ -111,9 +158,9 @@ def extract_image_patches(x, ksizes, ssizes, padding="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
+83 -20
View File
@@ -18,7 +18,7 @@ from keras import backend as K
from keras.backend import theano_backend as KTH
import inspect
import numpy as np
from keras.backend.common import _FLOATX, floatx, _EPSILON, image_dim_ordering
from keras.backend.common import _FLOATX, floatx, _EPSILON, image_data_format
from keras.backend.theano_backend import _preprocess_conv3d_input
from keras.backend.theano_backend import _preprocess_conv3d_kernel
from keras.backend.theano_backend import _preprocess_conv3d_filter_shape
@@ -32,10 +32,73 @@ import itertools
py_all = all
def conv2d(x, kernel, strides=(1, 1), padding='valid', data_format='channels_first',
image_shape=None, filter_shape=None):
'''
padding: string, "same" or "valid".
'''
if data_format not in {'channels_first', 'channels_last'}:
raise Exception('Unknown data_format ' + str(data_format))
if data_format == 'channels_last':
# TF uses the last dimension as channel dimension,
# instead of the 2nd one.
# TH input shape: (samples, input_depth, rows, cols)
# TF input shape: (samples, rows, cols, input_depth)
# TH kernel shape: (depth, input_depth, rows, cols)
# TF kernel shape: (rows, cols, input_depth, depth)
x = x.dimshuffle((0, 3, 1, 2))
kernel = kernel.dimshuffle((3, 2, 0, 1))
if image_shape:
image_shape = (image_shape[0], image_shape[3],
image_shape[1], image_shape[2])
if filter_shape:
filter_shape = (filter_shape[3], filter_shape[2],
filter_shape[0], filter_shape[1])
if padding == 'same':
th_padding = 'half'
np_kernel = kernel.eval()
elif padding == 'valid':
th_padding = 'valid'
else:
raise Exception('Border mode not supported: ' + str(padding))
# Theano might not accept long type
def int_or_none(value):
try:
return int(value)
except TypeError:
return None
if image_shape is not None:
image_shape = tuple(int_or_none(v) for v in image_shape)
if filter_shape is not None:
filter_shape = tuple(int_or_none(v) for v in filter_shape)
conv_out = T.nnet.conv2d(x, kernel,
border_mode=th_padding,
subsample=strides,
input_shape=image_shape,
filter_shape=filter_shape)
if padding == 'same':
if np_kernel.shape[2] % 2 == 0:
conv_out = conv_out[:, :, :(x.shape[2] + strides[0] - 1) // strides[0], :]
if np_kernel.shape[3] % 2 == 0:
conv_out = conv_out[:, :, :, :(x.shape[3] + strides[1] - 1) // strides[1]]
if data_format == 'channels_last':
conv_out = conv_out.dimshuffle((0, 2, 3, 1))
return conv_out
def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
border_mode='valid',
dim_ordering='default',
image_shape=None, filter_shape=None):
padding='valid',
data_format=None, filter_shape=None):
'''3D deconvolution (transposed convolution).
# Arguments
@@ -43,22 +106,22 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
output_shape: desired dimensions of output.
strides: strides tuple.
padding: string, "same" or "valid".
dim_ordering: "tf" or "th".
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 is None:
data_format = image_data_format()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown 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_padding = _preprocess_padding(padding)
@@ -68,7 +131,7 @@ 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(filter_shape, data_format)
filter_shape = tuple(filter_shape[i] for i in (1, 0, 2, 3, 4))
conv_out = T.nnet.abstract_conv.conv3d_grad_wrt_inputs(
@@ -79,11 +142,11 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
filter_flip=not flip_filters)
conv_out = _postprocess_conv3d_output(conv_out, x, padding,
kernel_shape, strides, dim_ordering)
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
@@ -92,7 +155,7 @@ def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering=
ksizes : 2-d tuple with the kernel size
strides : 2-d tuple with the strides size
padding : 'same' or 'valid'
dim_ordering : 'tf' or 'th'
data_format : 'channels_last' or 'channels_first'
Returns
-------
The (k_w,k_h) patches extracted
@@ -102,7 +165,7 @@ def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering=
patch_size = ksizes[1]
if padding == "same":
padding = "ignore_borders"
if dim_ordering == "tf":
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)
@@ -116,7 +179,7 @@ def extract_image_patches(X, ksizes, strides, border_mode="valid", dim_ordering=
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 +187,7 @@ 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 +198,7 @@ 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
tests/keras_contrib/backend/test_backends.py → tests/keras_contrib/backend/backend_test.py
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