Python cntk.convolution() Examples
The following are 30
code examples of cntk.convolution().
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Example #1
| Source File: cntk_backend.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
data_format = normalize_data_format(data_format)
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dev.type() == 0 and dilation_rate != (1, 1):
raise ValueError('Dilated convolution on CPU is not supported by CNTK backend. '
'Please set `dilation_rate` to (1, 1). '
'You passed: %s' % (dilation_rate,))
dilation_rate = (1,) + dilation_rate
x = C.convolution(kernel,
x,
strides,
auto_padding=[False, padding, padding],
dilation=dilation_rate)
return _postprocess_conv2d_output(x, data_format)
Example #2
| Source File: cntk_backend.py From keras-lambda with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #3
| Source File: cntk_backend.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #4
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #5
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #6
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #7
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #8
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #9
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #10
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
|
def conv3d(x, kernel, strides=(1, 1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1, 1)):
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))
x = _preprocess_conv3d_input(x, data_format)
kernel = _preprocess_conv3d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
strides = strides + (strides[0],)
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding,
padding])
return _postprocess_conv3d_output(x, data_format)
Example #11
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding])
else:
assert dilation_rate[0] == dilation_rate[1]
assert strides == (1, 1), 'Invalid strides for dilated convolution'
x = C.convolution(
kernel,
x,
strides=dilation_rate[0],
auto_padding=[
False,
padding,
padding])
return _postprocess_conv2d_output(x, data_format)
Example #12
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding],
groups=x.shape[0])
return _postprocess_conv2d_output(x, data_format)
Example #13
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
pointwise_kernel = _preprocess_conv2d_kernel(pointwise_kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
return _postprocess_conv2d_output(x, data_format)
Example #14
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding],
groups=x.shape[0])
return _postprocess_conv2d_output(x, data_format)
Example #15
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def conv1d(x, kernel, strides=1, padding='valid',
data_format=None, dilation_rate=1):
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 padding == 'causal':
# causal (dilated) convolution:
left_pad = dilation_rate * (kernel.shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
kernel = C.swapaxes(kernel, 0, 2)
padding = _preprocess_border_mode(padding)
strides = [strides]
x = C.convolution(
kernel,
x,
strides=tuple(strides),
auto_padding=[
False,
padding])
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
return x
Example #16
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding])
else:
assert dilation_rate[0] == dilation_rate[1]
assert strides == (1, 1), 'Invalid strides for dilated convolution'
x = C.convolution(
kernel,
x,
strides=dilation_rate[0],
auto_padding=[
False,
padding,
padding])
return _postprocess_conv2d_output(x, data_format)
Example #17
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
pointwise_kernel = _preprocess_conv2d_kernel(pointwise_kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
return _postprocess_conv2d_output(x, data_format)
Example #18
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding],
groups=x.shape[0])
return _postprocess_conv2d_output(x, data_format)
Example #19
| Source File: cntk_backend.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
data_format = normalize_data_format(data_format)
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding],
groups=x.shape[0])
return _postprocess_conv2d_output(x, data_format)
Example #20
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding])
else:
assert dilation_rate[0] == dilation_rate[1]
assert strides == (1, 1), 'Invalid strides for dilated convolution'
x = C.convolution(
kernel,
x,
strides=dilation_rate[0],
auto_padding=[
False,
padding,
padding])
return _postprocess_conv2d_output(x, data_format)
Example #21
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
pointwise_kernel = _preprocess_conv2d_kernel(pointwise_kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
return _postprocess_conv2d_output(x, data_format)
Example #22
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def depthwise_conv2d(x, depthwise_kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding],
groups=x.shape[0])
return _postprocess_conv2d_output(x, data_format)
Example #23
| Source File: cntk_backend.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def separable_conv2d(x, depthwise_kernel, pointwise_kernel, strides=(1, 1),
padding='valid', data_format=None, dilation_rate=(1, 1)):
data_format = normalize_data_format(data_format)
x = _preprocess_conv2d_input(x, data_format)
depthwise_kernel = _preprocess_conv2d_kernel(depthwise_kernel, data_format)
depthwise_kernel = C.reshape(C.transpose(depthwise_kernel, (1, 0, 2, 3)),
(-1, 1) + depthwise_kernel.shape[2:])
pointwise_kernel = _preprocess_conv2d_kernel(pointwise_kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(depthwise_kernel, x,
strides=strides,
auto_padding=[False, padding, padding],
groups=x.shape[0])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
else:
if dilation_rate[0] != dilation_rate[1]:
raise ValueError('CNTK Backend: non-square dilation_rate is '
'not supported.')
if strides != (1, 1):
raise ValueError('Invalid strides for dilated convolution')
x = C.convolution(depthwise_kernel, x,
strides=dilation_rate[0],
auto_padding=[False, padding, padding])
x = C.convolution(pointwise_kernel, x,
strides=(1, 1, 1),
auto_padding=[False])
return _postprocess_conv2d_output(x, data_format)
Example #24
| Source File: cntk_backend.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def conv1d(x, kernel, strides=1, padding='valid',
data_format=None, dilation_rate=1):
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 padding == 'causal':
# causal (dilated) convolution:
left_pad = dilation_rate * (kernel.shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
kernel = C.swapaxes(kernel, 0, 2)
padding = _preprocess_border_mode(padding)
strides = [strides]
x = C.convolution(
kernel,
x,
strides=tuple(strides),
auto_padding=[
False,
padding])
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
return x
Example #25
| Source File: cntk_backend.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding])
else:
assert dilation_rate[0] == dilation_rate[1]
assert strides == (1, 1), 'Invalid strides for dilated convolution'
x = C.convolution(
kernel,
x,
strides=dilation_rate[0],
auto_padding=[
False,
padding,
padding])
return _postprocess_conv2d_output(x, data_format)
Example #26
| Source File: cntk_backend.py From keras-lambda with MIT License | 5 votes |
|
def conv1d(x, kernel, strides=1, padding='valid',
data_format=None, dilation_rate=1):
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 padding == 'causal':
# causal (dilated) convolution:
left_pad = dilation_rate * (kernel.shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
kernel = C.swapaxes(kernel, 0, 2)
padding = _preprocess_border_mode(padding)
strides = [strides]
x = C.convolution(
kernel,
x,
strides=tuple(strides),
auto_padding=[
False,
padding])
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
return x
Example #27
| Source File: cntk_backend.py From keras-lambda with MIT License | 5 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding])
else:
assert dilation_rate[0] == dilation_rate[1]
assert strides == (1, 1), 'Invalid strides for dilated convolution'
x = C.convolution(
kernel,
x,
strides=dilation_rate[0],
auto_padding=[
False,
padding,
padding])
return _postprocess_conv2d_output(x, data_format)
Example #28
| Source File: cntk_backend.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def conv1d(x, kernel, strides=1, padding='valid',
data_format=None, dilation_rate=1):
data_format = normalize_data_format(data_format)
if padding == 'causal':
# causal (dilated) convolution:
left_pad = dilation_rate * (kernel.shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
# As of Keras 2.0.0, all kernels are normalized
# on the format `(steps, input_depth, depth)`,
# independently of `data_format`.
# CNTK expects `(depth, input_depth, steps)`.
kernel = C.swapaxes(kernel, 0, 2)
padding = _preprocess_border_mode(padding)
if dev.type() == 0 and dilation_rate != 1:
raise ValueError('Dilated convolution on CPU is not supported by CNTK backend. '
'Please set `dilation_rate` to 1. You passed: %s' % (dilation_rate,))
dilation_rate = (1, dilation_rate)
x = C.convolution(
kernel,
x,
strides=strides,
auto_padding=[False, padding],
dilation=dilation_rate)
if data_format == 'channels_last':
x = C.swapaxes(x, 0, 1)
return x
Example #29
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding])
else:
assert dilation_rate[0] == dilation_rate[1]
assert strides == (1, 1), 'Invalid strides for dilated convolution'
x = C.convolution(
kernel,
x,
strides=dilation_rate[0],
auto_padding=[
False,
padding,
padding])
return _postprocess_conv2d_output(x, data_format)
Example #30
| Source File: cntk_backend.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def conv2d(x, kernel, strides=(1, 1), padding='valid',
data_format=None, dilation_rate=(1, 1)):
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))
x = _preprocess_conv2d_input(x, data_format)
kernel = _preprocess_conv2d_kernel(kernel, data_format)
padding = _preprocess_border_mode(padding)
if dilation_rate == (1, 1):
strides = (1,) + strides
x = C.convolution(
kernel,
x,
strides,
auto_padding=[
False,
padding,
padding])
else:
assert dilation_rate[0] == dilation_rate[1]
assert strides == (1, 1), 'Invalid strides for dilated convolution'
x = C.convolution(
kernel,
x,
strides=dilation_rate[0],
auto_padding=[
False,
padding,
padding])
return _postprocess_conv2d_output(x, data_format)
