970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
120 lines
5.0 KiB
Python
120 lines
5.0 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 math
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import numpy as np
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from extensions.ops.MatMul import MatMul
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from extensions.ops.elementwise import Add, Mul
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from extensions.ops.transpose import Transpose
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.common.replacement import FrontReplacementSubgraph
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from mo.front.subgraph_matcher import SubgraphMatch
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from mo.graph.graph import Graph, rename_nodes
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from mo.ops.reshape import Reshape
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class FullyConnectedDecomposer(FrontReplacementSubgraph):
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"""
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Decomposes FC operation:
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1. Biases are added separately with the help of Add node
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2. FC node itself is converted to MatMul
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"""
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enabled = True
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def pattern(self):
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return dict(
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nodes=[('op', dict(kind='op', type='FullyConnected'))],
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edges=[]
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)
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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node = match['op']
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name = node.soft_get('name', node.id)
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# biases normalization
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if 2 in node.in_ports() and not node.in_port(2).disconnected():
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bias_node = Add(graph, {'name': name + '/Bias_'}).create_node()
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node_name = node.name + '/WithoutBiases'
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bias_node_name = node.name
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rename_nodes([(node, node_name), (bias_node, bias_node_name)])
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node.out_port(0).get_connection().set_source(bias_node.out_port(0))
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node.in_port(2).get_connection().set_destination(bias_node.in_port(1))
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node.out_port(0).connect(bias_node.in_port(0))
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# weights normalization
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assert node.has_valid('out-size')
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out_size = node['out-size']
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reshape_dim = int64_array([-1, out_size])
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if node.has_and_set('transpose_weights'):
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reshape_dim = int64_array([out_size, -1])
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node.insert_op_on_input_port(in_port_idx=1, new_op_class=Reshape,
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new_op_attrs={'name': name + '/weights_reshape'}, value=reshape_dim)
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if node.has_and_set('transpose_weights'):
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node.insert_op_on_input_port(in_port_idx=1, new_op_class=Transpose,
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new_op_attrs={'name': name + '/weights_transpose'}, value=int64_array([1, 0]))
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# input normalization for 4D Caffe and MxNet FullyConnected
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if graph.graph['fw'] in ['caffe', 'mxnet']:
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node.insert_op_on_input_port(in_port_idx=0, new_op_class=Reshape,
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new_op_attrs={'name': name + '/flatten_fc_input'}, value=int64_array([0, -1]))
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MatMul.update_node_stat(node, {})
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class GemmDecomposer(FrontReplacementSubgraph):
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"""
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Decomposes Gemm operation:
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1. Biases are added separately with the help of Add node
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2. Multiplication by `alpha` and `beta` values are separated to Mul operations
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3. Gemm operation itself is converted to MatMul
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"""
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enabled = True
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def pattern(self):
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return dict(
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nodes=[('op', dict(kind='op', op='Gemm'))],
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edges=[],
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)
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def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
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node = match['op']
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name = node.soft_get('name', node.id)
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# biases normalization
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bias_node = Add(graph, {'name': name + '/Bias_', 'can_be_scaleshift': False}).create_node()
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node_name = node.name + '/WithoutBiases'
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bias_node_name = node.name
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rename_nodes([(node, node_name), (bias_node, bias_node_name)])
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node.out_port(0).get_connection().set_source(bias_node.out_port(0))
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node.in_port(2).get_connection().set_destination(bias_node.in_port(1))
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node.out_port(0).connect(bias_node.in_port(0))
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if node.has_valid('alpha') and not math.isclose(node.alpha, 1):
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bias_node.insert_op_on_input_port(in_port_idx=0, new_op_class=Mul, value=np.array(node.alpha),
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new_op_attrs={'name': name + '/Alpha_', 'can_be_scaleshift': False})
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del node['alpha']
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if node.has_valid('beta') and not math.isclose(node.beta, 1):
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bias_node.insert_op_on_input_port(in_port_idx=1, new_op_class=Mul, value=np.array(node.beta),
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new_op_attrs={'name': name + '/Beta_', 'can_be_scaleshift': False})
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del node['beta']
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MatMul.update_node_stat(node, {
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'transpose_a': node.has_and_set('transpose_a'),
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'transpose_b': node.has_and_set('transpose_b'),
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})
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