Evgeny Lazarev
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
83 lines
3.3 KiB
Python
83 lines
3.3 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 extensions.ops.split import VariadicSplit
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from mo.back.replacement import BackReplacementPattern
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.tf.graph_utils import create_op_node_with_second_input, create_op_with_const_inputs
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from mo.graph.graph import Graph
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from mo.ops.const import Const
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from mo.ops.reshape import Reshape
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class CellNormalizer(BackReplacementPattern):
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# This class splits WR input on W and R for LSTMCell, GRUCell, RNNCell
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enabled = True
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force_clean_up = True
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def pattern(self):
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return dict(
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nodes=[
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('cell', dict(type=lambda type: type in ['LSTMCell', 'GRUCell', 'RNNCell']))
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],
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edges=[]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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node = match['cell']
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cell_name = node.soft_get('name', node.id)
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cell_type = node.soft_get('type')
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WR_input_id = node.soft_get('wr_input_id')
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hidden_size_coef = node.soft_get('gates_count')
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hidden_size = node.get_attrs()["hidden_size"]
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# default values for RNNCell/GRUCell
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additional_port_id = 4
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if cell_type == "LSTMCell":
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additional_port_id = 5
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WR_shape = node.in_port(WR_input_id).data.get_shape()
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assert WR_shape is not None, "Undefined 'WR' input shape for Cell node '{}'".format(cell_name)
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num_elements_in_WR = np.prod(WR_shape)
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input_size = (num_elements_in_WR / (hidden_size_coef * hidden_size)) - hidden_size
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# Reshape
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reshape = create_op_node_with_second_input(graph, Reshape,
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int64_array([hidden_size_coef * hidden_size,
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hidden_size + input_size]),
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{'name': cell_name + '/Dims'})
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# VariadicSplit
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split = create_op_with_const_inputs(graph, VariadicSplit, {1: int64_array(1),
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2: int64_array([input_size, hidden_size])},
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{'out_ports_count': 2, 'name': cell_name + '/Split'},
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reshape)
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# Cell
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node.in_port(WR_input_id).get_connection().set_destination(reshape.in_port(0))
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node.add_input_port(additional_port_id, skip_if_exist=True)
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assert node.in_port(additional_port_id).disconnected()
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# (x, y, WR, B) -> (x, y, W, R, B(additional_port))
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node.in_port(additional_port_id - 1).get_connection().set_destination(node.in_port(additional_port_id))
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split.out_port(0).connect(node.in_port(additional_port_id - 2))
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split.out_port(1).connect(node.in_port(additional_port_id - 1))
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