def spatial_2d_padding(x,
padding=((1, 1), (1, 1)),
mode='REFLECT',
data_format=None):
""" Pads the 2nd and 3rd dimensions of a 4D tensor.
# Arguments
x: Tensor or variable.
padding: Tuple of 2 tuples, padding pattern.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A padded 4D tensor.
# Raises
ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
"""
assert len(padding) == 2
assert len(padding[0]) == 2
assert len(padding[1]) == 2
data_format = normalize_data_format(data_format)
pattern = [[0, 0], list(padding[0]), list(padding[1]), [0, 0]]
pattern = transpose_shape(pattern, data_format, spatial_axes=(1, 2))
return tf.pad(x, pattern, mode)
def N_conv(x,
kernel,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)):
data_format = normalize_data_format(data_format)
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
# TF 2 arg conversion
kwargs = {}
if _is_tf_1():
kwargs['dilation_rate'] = dilation_rate
else:
kwargs['dilations'] = dilation_rate
x = conv2d_cosnorm(x, kernel, strides=strides, padding=padding)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = tf.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
def conv2d(x,
kernel,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1)):
"""2D convolution.
# Arguments
x: Tensor or variable.
kernel: kernel tensor.
strides: strides tuple.
padding: string, `"same"`, `"causal"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
Whether to use Theano or TensorFlow/CNTK data format
for inputs/kernels/outputs.
dilation_rate: tuple of 2 integers.
# Returns
A tensor, result of 2D convolution.
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = K.normalize_data_format(data_format)
# ADDED
kernel_shape = kernel.get_shape().as_list()
if padding == 'causal':
if data_format != 'channels_last':
raise ValueError('When using causal padding in `conv2d`, '
'`data_format` must be "channels_last" '
'(temporal data).')
# causal (dilated) convolution:
left_pad = dilation_rate[0] * (kernel_shape[0] - 1)
x = temporal_2d_padding(x, (left_pad, 0), data_format=data_format)
padding = 'valid'
# ADDED till here
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
x = tf.nn.convolution(input=x,
filter=kernel,
dilation_rate=dilation_rate,
strides=strides,
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = tf.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
"""2D convolution with separable filters.
# Arguments
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
pointwise_kernel: kernel for the 1x1 convolution.
strides: strides tuple (length 2).
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: tuple of integers,
dilation rates for the separable convolution.
# Returns
Output tensor.
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
# ADDED
kernel_shape = depthwise_kernel.get_shape().as_list()
if padding == 'causal':
if data_format != 'channels_last':
raise ValueError('When using causal padding in `conv1d`, '
'`data_format` must be "channels_last" '
'(temporal data).')
left_pad = dilation_rate * (kernel_shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
# ADDED till here
x, tf_data_format = _preprocess_conv2d_input(x, data_format)
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
strides = (1,) + strides + (1,)
else:
strides = (1, 1) + strides
x = tf.nn.separable_conv2d(x, depthwise_kernel, pointwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = tf.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
return x
def conv1d(x, kernel, strides=1, padding='valid',
data_format=None, dilation_rate=1):
"""1D convolution.
# Arguments
x: Tensor or variable.
kernel: kernel tensor.
strides: stride integer.
padding: string, `"same"`, `"causal"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: integer dilate rate.
# Returns
A tensor, result of 1D convolution.
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
kernel_shape = kernel.get_shape().as_list()
if padding == 'causal':
if data_format != 'channels_last':
raise ValueError('When using causal padding in `conv1d`, '
'`data_format` must be "channels_last" '
'(temporal data).')
# causal (dilated) convolution:
left_pad = dilation_rate * (kernel_shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
padding = _preprocess_padding(padding)
x, tf_data_format = _preprocess_conv1d_input(x, data_format)
x = tf.nn.convolution(
input=x,
filter=kernel,
dilation_rate=(dilation_rate,),
strides=(strides,),
padding=padding,
data_format=tf_data_format)
if data_format == 'channels_first' and tf_data_format == 'NWC':
x = tf.transpose(x, (0, 2, 1)) # NWC -> NCW
return x
def separable_conv1d(x, depthwise_kernel, pointwise_kernel, strides=1,
padding='valid', data_format=None, dilation_rate=1):
"""1D convolution with separable filters.
# Arguments
x: input tensor
depthwise_kernel: convolution kernel for the depthwise convolution.
pointwise_kernel: kernel for the 1x1 convolution.
strides: stride integer.
padding: string, `"same"` or `"valid"`.
data_format: string, `"channels_last"` or `"channels_first"`.
dilation_rate: integer dilation rate.
# Returns
Output tensor.
# Raises
ValueError: If `data_format` is neither
`"channels_last"` nor `"channels_first"`.
"""
data_format = normalize_data_format(data_format)
if isinstance(strides, int):
strides = (strides,)
if isinstance(dilation_rate, int) and False:
dilation_rate = (dilation_rate,)
x, tf_data_format = _preprocess_conv1d_input(x, data_format)
if tf_data_format == 'NWC':
tf_data_format = 'NHWC'
else:
tf_data_format = 'NCHW'
# ADDED
kernel_shape = depthwise_kernel.get_shape().as_list()
if padding == 'causal':
if data_format != 'channels_last':
raise ValueError('When using causal padding in `conv1d`, '
'`data_format` must be "channels_last" '
'(temporal data).')
# causal (dilated) convolution:
tmp_d = dilation_rate[0]\
if isinstance(dilation_rate, tuple) \
else dilation_rate
left_pad = tmp_d * (kernel_shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
# ADDED till here
padding = _preprocess_padding(padding)
if tf_data_format == 'NHWC':
spatial_start_dim = 1
strides = (1,) + strides * 2 + (1,)
else:
spatial_start_dim = 2
strides = (1, 1) + strides * 2
x = tf.expand_dims(x, spatial_start_dim)
depthwise_kernel = tf.expand_dims(depthwise_kernel, 0)
pointwise_kernel = tf.expand_dims(pointwise_kernel, 0)
dilation_rate = (1,) + dilation_rate
x = tf.nn.separable_conv2d(x, depthwise_kernel, pointwise_kernel,
strides=strides,
padding=padding,
rate=dilation_rate,
data_format=tf_data_format)
x = tf.squeeze(x, [spatial_start_dim])
if data_format == 'channels_first' and tf_data_format == 'NHWC':
x = tf.transpose(x, (0, 2, 1)) # NWC -> NCW
return x
