Yegor Kruglovandyegor.kruglov
98fffe7f22
* initial commit * initial commit * move fix to tf conv_extractor * added 3d case * fix e2e with 3d conv * remove 3d case Co-authored-by: yegor.kruglov <ykruglov@nnlvdp-mkaglins.inn.intel.com>
113 lines
4.5 KiB
Python
113 lines
4.5 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from mo.front.common.partial_infer.utils import convert_tf_padding_to_str, int64_array
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from mo.front.extractor import FrontExtractorOp
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from mo.front.tf.extractors.utils import tf_data_format_channel, tf_data_format_batch, \
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tf_int_list
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from mo.ops.convolution import Convolution
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from mo.ops.op import PermuteAttrs
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class Conv2DFrontExtractor(FrontExtractorOp):
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op = 'Conv2D'
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enabled = True
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@classmethod
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def extract(cls, node):
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attrs = tf_create_attrs(node, 2, 3)
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attrs.update({'op': __class__.op,
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'get_group': lambda node: node.group if 'group' in node and node.group is not None else
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node.in_node(0).shape[node.channel_dims] // node.kernel_shape[node.input_feature_channel],
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'get_output_feature_dim': lambda node: node.kernel_shape[node.output_feature_channel],
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'get_weights_permute': PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),
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inv=int64_array([2, 3, 1, 0]))
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})
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# update the attributes of the node
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Convolution.update_node_stat(node, attrs)
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return cls.enabled
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class DepthwiseConv2dNativeFrontExtractor(FrontExtractorOp):
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op = 'DepthwiseConv2dNative'
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enabled = True
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@classmethod
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def extract(cls, node):
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attrs = tf_create_attrs(node, 2, 2)
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attrs.update({'op': __class__.op,
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'kernel_spatial_idx': np.array([0, 1], dtype=np.int64),
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'get_group': lambda node: node.kernel_shape[node.output_feature_channel],
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'get_output_feature_dim': lambda node: node.kernel_shape[-1] * node.kernel_shape[-2],
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'get_weights_permute': PermuteAttrs.Permutation(perm=int64_array([2, 3, 0, 1]),
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inv=int64_array([2, 3, 0, 1]))
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})
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# update the attributes of the node
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Convolution.update_node_stat(node, attrs)
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return cls.enabled
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class Conv3DFrontExtractor(FrontExtractorOp):
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op = 'Conv3D'
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enabled = True
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@classmethod
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def extract(cls, node):
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attrs = tf_create_attrs(node, 3, 4)
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attrs.update({'op': __class__.op,
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'get_group': lambda node: 1,
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'get_output_feature_dim': lambda node: node.kernel_shape[node.output_feature_channel],
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'get_weights_permute': PermuteAttrs.Permutation(perm=int64_array([4, 3, 0, 1, 2]),
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inv=int64_array([2, 3, 4, 1, 0]))
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})
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# update the attributes of the node
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Convolution.update_node_stat(node, attrs)
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return cls.enabled
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def tf_create_attrs(node, input_feature_channel, output_feature_channel):
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data_format = node.pb.attr["data_format"]
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dilations = tf_int_list(node.pb.attr["dilations"].list)
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if len(dilations) == 0:
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dilations = None
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attrs = {
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'type': 'Convolution',
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'auto_pad': convert_tf_padding_to_str(node.pb.attr['padding'].s.decode()),
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'bias_addable': True,
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'bias_term': False,
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'dilation': dilations,
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'stride': tf_int_list(node.pb.attr["strides"].list),
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'channel_dims': tf_data_format_channel(data_format),
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'batch_dims': tf_data_format_batch(data_format),
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'input_feature_channel': input_feature_channel,
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'output_feature_channel': output_feature_channel,
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'layout': data_format.s.decode(),
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# get_group and get_output_feature_dim are special attrs that stores lambdas ( lambda node, kernel_shape:...)
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# this attrs calls in infer function to calculate output feature dimension and group attr
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'get_group': None, # lambda should return group attr for given node
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'get_output_feature_dim': None, # lamda should return output feature dimension
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}
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return attrs
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