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"""
Copyright (c) 2018 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import numpy as np
from mo.front.caffe.extractors.utils import get_spatial_attr, get_list_from_container, weights_biases
from mo.front.common.extractors.utils import layout_attrs
from mo.front.extractor import FrontExtractorOp
from mo.ops.convolution import Convolution
from mo.utils.error import Error
class ConvFrontExtractor(FrontExtractorOp):
op = 'convolution'
enabled = True
@staticmethod
def extract(node):
proto_layer, model_layer = node.pb, node.model_pb
if not proto_layer:
raise Error('Protobuf layer can not be empty')
conv_param = proto_layer.convolution_param
conv_type = 'ConvND' if len(proto_layer.bottom) > 1 else 'Conv2D'
params = conv_set_params(conv_param, conv_type)
attrs = conv_create_attrs(params)
attrs.update({'op': conv_type,
'get_group': lambda node: node.group,
'get_output_feature_dim': lambda node: node.output
})
# Embed weights and biases as attributes
# It will be moved to a separate nodes in special pass
attrs.update(
weights_biases(conv_param.bias_term, model_layer, start_index=len(proto_layer.bottom), proto=conv_param))
attrs.update(layout_attrs())
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return __class__.enabled
class DeconvFrontExtractor(FrontExtractorOp):
op = 'deconvolution'
enabled = True
@staticmethod
def extract(node):
proto_layer, model_layer = node.pb, node.model_pb
if not proto_layer:
raise Error('Protobuf layer can not be empty')
deconv_param = proto_layer.convolution_param
params = conv_set_params(deconv_param, 'Deconv2D')
attrs = conv_create_attrs(params)
attrs.update({'type': 'Deconvolution',
'op': 'Deconv2D',
'get_group': lambda node: node.group,
'get_output_feature_dim': lambda node: node.output,
'input_feature_channel': 0,
'output_feature_channel': 1,
})
# Embed weights and biases as attributes
# It will be moved to a separate nodes in special pass
attrs.update(weights_biases(deconv_param.bias_term, model_layer))
attrs.update(layout_attrs())
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return __class__.enabled
def conv_create_attrs(params):
"""
Creates object of attrs for convolution
Args:
params: {
type_str: type_str
padding: padding
dilate: dilate
stride: stride
kernel: kernel
group: group
output: output
bias_term: bias_term
}
Returns:
object with all necessary convolution attributes
"""
return {
'bias_addable': True,
'bias_term': params['bias_term'],
'pad': np.array([[0, 0], [0, 0], [params['padding'][1], params['padding'][1]],
[params['padding'][0], params['padding'][0]]], dtype=np.int64),
'pad_spatial_shape': np.array([[params['padding'][1], params['padding'][1]],
[params['padding'][0], params['padding'][0]]], dtype=np.int64),
'dilation': np.array([1, 1, params['dilate'][1], params['dilate'][0]], dtype=np.int64),
'output_spatial_shape': None,
'output_shape': None,
'stride': np.array([1, 1, params['stride'][1], params['stride'][0]], dtype=np.int64),
'group': params['group'],
'output': params['output'],
'kernel_spatial': np.array([params['kernel'][1], params['kernel'][0]], dtype=np.int64),
'kernel_spatial_idx': np.array([2, 3], dtype=np.int64),
'reshape_kernel': True,
'input_feature_channel': 1,
'output_feature_channel': 0,
}
def conv_set_params(conv_param, conv_type):
# Defaults
padding = [0, 0]
stride = [1, 1]
kernel = [0, 0]
dilate = [1, 1]
group = 1
kernel = get_spatial_attr(kernel, 'kernel_size', 'kernel', conv_param)
padding = get_spatial_attr(padding, 'pad', 'pad', conv_param)
stride = get_spatial_attr(stride, 'stride', 'stride', conv_param)
dilates = get_list_from_container(conv_param, 'dilation', int)
if len(dilates) > 0:
dilate[0] = dilate[1] = dilates[0]
groups = get_list_from_container(conv_param, 'group', int)
group = groups[0] if len(groups) > 0 and groups[0] != 1 else group
return {
'type_str': conv_type,
'padding': padding,
'dilate': dilate,
'stride': stride,
'kernel': kernel,
'group': group,
'output': conv_param.num_output,
'bias_term': conv_param.bias_term
}
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