|
|
|
|
@ -21,6 +21,7 @@ from ..framework import Variable
|
|
|
|
|
from ..param_attr import ParamAttr
|
|
|
|
|
from layer_function_generator import autodoc
|
|
|
|
|
from tensor import concat
|
|
|
|
|
import utils
|
|
|
|
|
|
|
|
|
|
__all__ = [
|
|
|
|
|
'fc',
|
|
|
|
|
@ -1138,8 +1139,8 @@ def sequence_conv(input,
|
|
|
|
|
def conv2d(input,
|
|
|
|
|
num_filters,
|
|
|
|
|
filter_size,
|
|
|
|
|
stride=None,
|
|
|
|
|
padding=None,
|
|
|
|
|
stride=1,
|
|
|
|
|
padding=0,
|
|
|
|
|
groups=None,
|
|
|
|
|
param_attr=None,
|
|
|
|
|
bias_attr=None,
|
|
|
|
|
@ -1252,12 +1253,10 @@ def conv2d(input,
|
|
|
|
|
raise ValueError("num_channels must be divisible by groups.")
|
|
|
|
|
num_filter_channels = num_channels / groups
|
|
|
|
|
|
|
|
|
|
if isinstance(filter_size, int):
|
|
|
|
|
filter_size = [filter_size, filter_size]
|
|
|
|
|
if isinstance(stride, int):
|
|
|
|
|
stride = [stride, stride]
|
|
|
|
|
if isinstance(padding, int):
|
|
|
|
|
padding = [padding, padding]
|
|
|
|
|
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
|
|
|
|
|
stride = utils.convert_to_list(stride, 2, 'stride')
|
|
|
|
|
padding = utils.convert_to_list(padding, 2, 'padding')
|
|
|
|
|
|
|
|
|
|
if not isinstance(use_cudnn, bool):
|
|
|
|
|
raise ValueError("use_cudnn should be True or False")
|
|
|
|
|
|
|
|
|
|
@ -1432,10 +1431,10 @@ def sequence_last_step(input):
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def pool2d(input,
|
|
|
|
|
pool_size,
|
|
|
|
|
pool_type,
|
|
|
|
|
pool_stride=None,
|
|
|
|
|
pool_padding=None,
|
|
|
|
|
pool_size=-1,
|
|
|
|
|
pool_type="max",
|
|
|
|
|
pool_stride=1,
|
|
|
|
|
pool_padding=0,
|
|
|
|
|
global_pooling=False,
|
|
|
|
|
use_cudnn=True,
|
|
|
|
|
name=None):
|
|
|
|
|
@ -1443,20 +1442,20 @@ def pool2d(input,
|
|
|
|
|
This function adds the operator for pooling in 2 dimensions, using the
|
|
|
|
|
pooling configurations mentioned in input parameters.
|
|
|
|
|
"""
|
|
|
|
|
if pool_padding is None:
|
|
|
|
|
pool_padding = [0, 0]
|
|
|
|
|
if pool_stride is None:
|
|
|
|
|
pool_stride = [1, 1]
|
|
|
|
|
if pool_type not in ["max", "avg"]:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
|
|
|
|
|
str(pool_type))
|
|
|
|
|
if isinstance(pool_size, int):
|
|
|
|
|
pool_size = [pool_size, pool_size]
|
|
|
|
|
if isinstance(pool_stride, int):
|
|
|
|
|
pool_stride = [pool_stride, pool_stride]
|
|
|
|
|
if isinstance(pool_padding, int):
|
|
|
|
|
pool_padding = [pool_padding, pool_padding]
|
|
|
|
|
|
|
|
|
|
if global_pooling is False and pool_size == -1:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
"When the global_pooling is False, pool_size must be passed "
|
|
|
|
|
"and be a valid value. Received pool_size: " + str(pool_size))
|
|
|
|
|
|
|
|
|
|
pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
|
|
|
|
|
pool_padding = utils.convert_to_list(pool_padding, 2, 'pool_padding')
|
|
|
|
|
pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride')
|
|
|
|
|
|
|
|
|
|
if not isinstance(use_cudnn, bool):
|
|
|
|
|
raise ValueError("use_cudnn should be True or False")
|
|
|
|
|
|
|
|
|
|
@ -1685,9 +1684,9 @@ def conv2d_transpose(input,
|
|
|
|
|
num_filters,
|
|
|
|
|
output_size=None,
|
|
|
|
|
filter_size=None,
|
|
|
|
|
padding=None,
|
|
|
|
|
stride=None,
|
|
|
|
|
dilation=None,
|
|
|
|
|
padding=0,
|
|
|
|
|
stride=1,
|
|
|
|
|
dilation=1,
|
|
|
|
|
param_attr=None,
|
|
|
|
|
use_cudnn=True,
|
|
|
|
|
name=None):
|
|
|
|
|
@ -1783,26 +1782,12 @@ def conv2d_transpose(input,
|
|
|
|
|
raise TypeError("Input of conv2d_transpose must be Variable")
|
|
|
|
|
input_channel = input.shape[1]
|
|
|
|
|
|
|
|
|
|
op_attr = dict()
|
|
|
|
|
|
|
|
|
|
if isinstance(padding, int):
|
|
|
|
|
op_attr['paddings'] = [padding, padding]
|
|
|
|
|
elif padding is not None:
|
|
|
|
|
op_attr['paddings'] = padding
|
|
|
|
|
|
|
|
|
|
if isinstance(stride, int):
|
|
|
|
|
op_attr['strides'] = [stride, stride]
|
|
|
|
|
elif stride is not None:
|
|
|
|
|
op_attr['strides'] = stride
|
|
|
|
|
|
|
|
|
|
if isinstance(dilation, int):
|
|
|
|
|
op_attr['dilations'] = [dilation, dilation]
|
|
|
|
|
elif dilation is not None:
|
|
|
|
|
op_attr['dilations'] = dilation
|
|
|
|
|
padding = utils.convert_to_list(padding, 2, 'padding')
|
|
|
|
|
stride = utils.convert_to_list(stride, 2, 'stride')
|
|
|
|
|
dilation = utils.convert_to_list(dilation, 2, 'dilation')
|
|
|
|
|
|
|
|
|
|
if not isinstance(use_cudnn, bool):
|
|
|
|
|
raise ValueError("use_cudnn should be True or False")
|
|
|
|
|
op_attr['use_cudnn'] = use_cudnn
|
|
|
|
|
|
|
|
|
|
if filter_size is None:
|
|
|
|
|
if output_size is None:
|
|
|
|
|
@ -1810,10 +1795,6 @@ def conv2d_transpose(input,
|
|
|
|
|
if isinstance(output_size, int):
|
|
|
|
|
output_size = [output_size, output_size]
|
|
|
|
|
|
|
|
|
|
padding = op_attr.get('paddings', [0, 0])
|
|
|
|
|
stride = op_attr.get('strides', [1, 1])
|
|
|
|
|
dilation = op_attr.get('dilations', [1, 1])
|
|
|
|
|
|
|
|
|
|
h_in = input.shape[2]
|
|
|
|
|
w_in = input.shape[3]
|
|
|
|
|
|
|
|
|
|
@ -1822,9 +1803,9 @@ def conv2d_transpose(input,
|
|
|
|
|
filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 *
|
|
|
|
|
padding[1] - 1) / dilation[1] + 1
|
|
|
|
|
filter_size = [filter_size_h, filter_size_w]
|
|
|
|
|
|
|
|
|
|
elif isinstance(filter_size, int):
|
|
|
|
|
filter_size = [filter_size, filter_size]
|
|
|
|
|
else:
|
|
|
|
|
filter_size = utils.convert_to_list(filter_size, 2,
|
|
|
|
|
'conv2d_transpose.filter_size')
|
|
|
|
|
|
|
|
|
|
filter_shape = [input_channel, num_filters] + filter_size
|
|
|
|
|
img_filter = helper.create_parameter(
|
|
|
|
|
@ -1836,7 +1817,12 @@ def conv2d_transpose(input,
|
|
|
|
|
inputs={'Input': [input],
|
|
|
|
|
'Filter': [img_filter]},
|
|
|
|
|
outputs={'Output': out},
|
|
|
|
|
attrs=op_attr)
|
|
|
|
|
attrs={
|
|
|
|
|
'strides': stride,
|
|
|
|
|
'paddings': padding,
|
|
|
|
|
'dilations': dilation,
|
|
|
|
|
'use_cudnn': use_cudnn
|
|
|
|
|
})
|
|
|
|
|
|
|
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
|
wanghaox
7 years ago