|
|
|
@ -267,8 +267,8 @@ def conv1d(x,
|
|
|
|
|
dilation = utils.convert_to_list(dilation, 1, 'dilation') + [1]
|
|
|
|
|
|
|
|
|
|
l_type = "conv2d"
|
|
|
|
|
if (num_channels == groups and num_filters % num_channels == 0 and
|
|
|
|
|
not use_cudnn):
|
|
|
|
|
if (num_channels == groups and num_channels != 1 and
|
|
|
|
|
num_filters % num_channels == 0 and not use_cudnn):
|
|
|
|
|
l_type = 'depthwise_conv2d'
|
|
|
|
|
use_cudnn = False
|
|
|
|
|
|
|
|
|
@ -491,7 +491,8 @@ def conv2d(x,
|
|
|
|
|
dilation = utils.convert_to_list(dilation, 2, 'dilation')
|
|
|
|
|
|
|
|
|
|
l_type = "conv2d"
|
|
|
|
|
if (num_channels == groups and num_filters % num_channels == 0):
|
|
|
|
|
if (num_channels == groups and num_channels != 1 and
|
|
|
|
|
num_filters % num_channels == 0):
|
|
|
|
|
l_type = 'depthwise_conv2d'
|
|
|
|
|
use_cudnn = False
|
|
|
|
|
|
|
|
|
@ -761,7 +762,8 @@ def conv_transpose1d(x,
|
|
|
|
|
|
|
|
|
|
op_type = 'conv2d_transpose'
|
|
|
|
|
num_filters = weight.shape[1]
|
|
|
|
|
if (num_channels == groups and num_filters == 1 and not use_cudnn):
|
|
|
|
|
if (num_channels == groups and num_channels != 1 and num_filters == 1 and
|
|
|
|
|
not use_cudnn):
|
|
|
|
|
op_type = 'depthwise_conv2d_transpose'
|
|
|
|
|
use_cudnn = False
|
|
|
|
|
|
|
|
|
@ -1010,7 +1012,7 @@ def conv_transpose2d(x,
|
|
|
|
|
|
|
|
|
|
op_type = 'conv2d_transpose'
|
|
|
|
|
num_filters = weight.shape[1]
|
|
|
|
|
if (num_channels == groups and num_filters == 1):
|
|
|
|
|
if (num_channels == groups and num_channels != 1 and num_filters == 1):
|
|
|
|
|
op_type = 'depthwise_conv2d_transpose'
|
|
|
|
|
use_cudnn = False
|
|
|
|
|
|
|
|
|
|