def convert_pooling(net, node, module, builder):
"""Convert a pooling layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attrs(node)
layer_type_mx = param['pool_type']
if layer_type_mx == 'max':
layer_type = 'MAX'
elif layer_type_mx == 'avg':
layer_type = 'AVERAGE'
else:
raise TypeError("Pooling type %s not supported" % layer_type_mx)
# Add padding if there is any
if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
pad = literal_eval(param['pad'])
builder.add_padding(
name=name+"_pad",
left=pad[1],
right=pad[1],
top=pad[0],
bottom=pad[0],
value=0,
input_name=input_name,
output_name=name+"_pad_output")
input_name = name+"_pad_output"
stride_height = 1
stride_width = 1
if 'stride' in param.keys():
stride_height, stride_width = literal_eval(param['stride'])
kernel_width, kernel_height = literal_eval(param['kernel'])
type_map = {'valid': 'VALID', 'full': 'INCLUDE_LAST_PIXEL'}
padding_type = param['pooling_convention'] if 'pooling_convention' in param else 'valid'
if padding_type not in type_map:
raise KeyError("%s type is not supported in this converter. It is a Github issue.")
padding_type = type_map[padding_type]
if 'global_pool' in param.keys():
is_global = literal_eval(param['global_pool'])
else:
is_global = False
# For reasons why we are not using the standard builder but having our own implementation,
# see the function documentation.
_add_pooling.add_pooling_with_padding_types(
builder=builder,
name=name,
height=kernel_height,
width=kernel_width,
stride_height=stride_height,
stride_width=stride_width,
layer_type=layer_type,
padding_type=padding_type,
exclude_pad_area=False,
is_global=is_global,
input_name=input_name,
output_name=output_name
)
def convert_pooling(net, node, module, builder):
"""Convert a pooling layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = node['attr']
layer_type_mx = param['pool_type']
if layer_type_mx == 'max':
layer_type = 'MAX'
elif layer_type_mx == 'avg':
layer_type = 'AVERAGE'
else:
raise TypeError("Pooling type %s not supported" % layer_type_mx)
# Add padding if there is any
if literal_eval(param['pad']) != (0, 0):
pad = literal_eval(param['pad'])
builder.add_padding(
name=name+"_pad",
left=pad[1],
right=pad[1],
top=pad[0],
bottom=pad[0],
value=0,
input_name=input_name,
output_name=name+"_pad_output")
input_name = name+"_pad_output"
stride_height, stride_width = literal_eval(param['stride'])
kernel_width, kernel_height = literal_eval(param['kernel'])
type_map = {'valid': 'VALID', 'full': 'INCLUDE_LAST_PIXEL'}
padding_type = param['pooling_convention'] if 'pooling_convention' in param else 'valid'
if padding_type not in type_map:
raise KeyError("%s type is not supported in this converter. It is a Github issue.")
padding_type = type_map[padding_type]
if 'global_pool' in param.keys():
is_global = literal_eval(param['global_pool'])
else:
is_global = False
# For reasons why we are not using the standard builder but having our own implementation,
# see the function documentation.
_add_pooling.add_pooling_with_padding_types(
builder=builder,
name=name,
height=kernel_height,
width=kernel_width,
stride_height=stride_height,
stride_width=stride_width,
layer_type=layer_type,
padding_type=padding_type,
exclude_pad_area=False,
is_global=is_global,
input_name=input_name,
output_name=output_name
)